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Publisher: Springer-Verlag (Total: 2351 journals)

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Showing 1 - 200 of 2351 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 1)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 22, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 15, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 26, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 27, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 18, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 10, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 6, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 2, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 23, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 16, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 55, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 28, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 9, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 17, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 12, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 8, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 8, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 16, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 13, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 5)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 19, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 18, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 30, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 7, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 13, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 8, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 43, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 17, SJR: 0.21, CiteScore: 1)
Applied Cancer Research     Open Access  
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 4, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 9, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 65, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 18, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 24, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 34, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 62, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 144, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 16, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 8, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 14, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 16, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 12, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 9)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 3, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  

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Journal Cover
Advances in Data Analysis and Classification
Journal Prestige (SJR): 1.09
Citation Impact (citeScore): 1
Number of Followers: 55  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1862-5355 - ISSN (Online) 1862-5347
Published by Springer-Verlag Homepage  [2351 journals]
  • Editorial for issue 3/2018
    • Pages: 449 - 454
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-018-0340-3
      Issue No: Vol. 12, No. 3 (2018)
       
  • Mutual information, phi-squared and model-based co-clustering for
           contingency tables
    • Authors: Gérard Govaert; Mohamed Nadif
      Pages: 455 - 488
      Abstract: Many of the datasets encountered in statistics are two-dimensional in nature and can be represented by a matrix. Classical clustering procedures seek to construct separately an optimal partition of rows or, sometimes, of columns. In contrast, co-clustering methods cluster the rows and the columns simultaneously and organize the data into homogeneous blocks (after suitable permutations). Methods of this kind have practical importance in a wide variety of applications such as document clustering, where data are typically organized in two-way contingency tables. Our goal is to offer coherent frameworks for understanding some existing criteria and algorithms for co-clustering contingency tables, and to propose new ones. We look at two different frameworks for the problem of co-clustering. The first involves minimizing an objective function based on measures of association and in particular on phi-squared and mutual information. The second uses a model-based co-clustering approach, and we consider two models: the block model and the latent block model. We establish connections between different approaches, criteria and algorithms, and we highlight a number of implicit assumptions in some commonly used algorithms. Our contribution is illustrated by numerical experiments on simulated and real-case datasets that show the relevance of the presented methods in the document clustering field.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-016-0274-6
      Issue No: Vol. 12, No. 3 (2018)
       
  • Model selection for Gaussian latent block clustering with the integrated
           classification likelihood
    • Authors: Aurore Lomet; Gérard Govaert; Yves Grandvalet
      Pages: 489 - 508
      Abstract: Block clustering aims to reveal homogeneous block structures in a data table. Among the different approaches of block clustering, we consider here a model-based method: the Gaussian latent block model for continuous data which is an extension of the Gaussian mixture model for one-way clustering. For a given data table, several candidate models are usually examined, which differ for example in the number of clusters. Model selection then becomes a critical issue. To this end, we develop a criterion based on an approximation of the integrated classification likelihood for the Gaussian latent block model, and propose a Bayesian information criterion-like variant following the same pattern. We also propose a non-asymptotic exact criterion, thus circumventing the controversial definition of the asymptotic regime arising from the dual nature of the rows and columns in co-clustering. The experimental results show steady performances of these criteria for medium to large data tables.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-013-0161-3
      Issue No: Vol. 12, No. 3 (2018)
       
  • Discovering patterns in time-varying graphs: a triclustering approach
    • Authors: Romain Guigourès; Marc Boullé; Fabrice Rossi
      Pages: 509 - 536
      Abstract: This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter tuning. The three dimensions are simultaneously segmented in order to build clusters of source vertices, destination vertices and time segments where the edge distributions across clusters of vertices follow the same evolution over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make any a priori quantization. Experiments conducted on artificial data illustrate the good behavior of the technique, and a study of a real-life data set shows the potential of the proposed approach for exploratory data analysis.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-015-0218-6
      Issue No: Vol. 12, No. 3 (2018)
       
  • Cluster-based sparse topical coding for topic mining and document
           clustering
    • Authors: Parvin Ahmadi; Iman Gholampour; Mahmoud Tabandeh
      Pages: 537 - 558
      Abstract: In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents collection. Experimental results on widely-used datasets show that our proposed method significantly outperforms the traditional and other topic model based clustering methods. Our method achieves from 4 to 39% improvement in clustering accuracy and from 2% to more than 44% improvement in normalized mutual information.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0280-3
      Issue No: Vol. 12, No. 3 (2018)
       
  • Sparsest factor analysis for clustering variables: a matrix decomposition
           approach
    • Authors: Kohei Adachi; Nickolay T. Trendafilov
      Pages: 559 - 585
      Abstract: We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0284-z
      Issue No: Vol. 12, No. 3 (2018)
       
  • Minimum distance method for directional data and outlier detection
    • Authors: Mercedes Fernandez Sau; Daniela Rodriguez
      Pages: 587 - 603
      Abstract: In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0287-9
      Issue No: Vol. 12, No. 3 (2018)
       
  • Statistical inference in constrained latent class models for multinomial
           data based on $$\phi $$ ϕ -divergence measures
    • Authors: A. Felipe; N. Martín; P. Miranda; L. Pardo
      Pages: 605 - 636
      Abstract: In this paper we explore the possibilities of applying \(\phi \) -divergence measures in inferential problems in the field of latent class models (LCMs) for multinomial data. We first treat the problem of estimating the model parameters. As explained below, minimum \(\phi \) -divergence estimators (M \(\phi \) Es) considered in this paper are a natural extension of the maximum likelihood estimator (MLE), the usual estimator for this problem; we study the asymptotic properties of M \(\phi \) Es, showing that they share the same asymptotic distribution as the MLE. To compare the efficiency of the M \(\phi \) Es when the sample size is not big enough to apply the asymptotic results, we have carried out an extensive simulation study; from this study, we conclude that there are estimators in this family that are competitive with the MLE. Next, we deal with the problem of testing whether a LCM for multinomial data fits a data set; again, \(\phi \) -divergence measures can be used to generate a family of test statistics generalizing both the classical likelihood ratio test and the chi-squared test statistics. Finally, we treat the problem of choosing the best model out of a sequence of nested LCMs; as before, \(\phi \) -divergence measures can handle the problem and we derive a family of \(\phi \) -divergence test statistics based on them; we study the asymptotic behavior of these test statistics, showing that it is the same as the classical test statistics. A simulation study for small and moderate sample sizes shows that there are some test statistics in the family that can compete with the classical likelihood ratio and the chi-squared test statistics.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0289-7
      Issue No: Vol. 12, No. 3 (2018)
       
  • A divisive clustering method for functional data with special
           consideration of outliers
    • Authors: Ana Justel; Marcela Svarc
      Pages: 637 - 656
      Abstract: This paper presents DivClusFD, a new divisive hierarchical method for the non-supervised classification of functional data. Data of this type present the peculiarity that the differences among clusters may be caused by changes as well in level as in shape. Different clusters can be separated in different subregion and there may be no subregion in which all clusters are separated. In each step of division, the DivClusFD method explores the functions and their derivatives at several fixed points, seeking the subregion in which the highest number of clusters can be separated. The number of clusters is estimated via the gap statistic. The functions are assigned to the new clusters by combining the k-means algorithm with the use of functional boxplots to identify functions that have been incorrectly classified because of their atypical local behavior. The DivClusFD method provides the number of clusters, the classification of the observed functions into the clusters and guidelines that may be for interpreting the clusters. A simulation study using synthetic data and tests of the performance of the DivClusFD method on real data sets indicate that this method is able to classify functions accurately.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0290-1
      Issue No: Vol. 12, No. 3 (2018)
       
  • Archetypal shapes based on landmarks and extension to handle missing data
    • Authors: Irene Epifanio; María Victoria Ibáñez; Amelia Simó
      Pages: 705 - 735
      Abstract: Archetype and archetypoid analysis are extended to shapes. The objective is to find representative shapes. Archetypal shapes are pure (extreme) shapes. We focus on the case where the shape of an object is represented by a configuration matrix of landmarks. As shape space is not a vectorial space, we work in the tangent space, the linearized space about the mean shape. Then, each observation is approximated by a convex combination of actual observations (archetypoids) or archetypes, which are a convex combination of observations in the data set. These tools can contribute to the understanding of shapes, as in the usual multivariate case, since they lie somewhere between clustering and matrix factorization methods. A new simplex visualization tool is also proposed to provide a picture of the archetypal analysis results. We also propose new algorithms for performing archetypal analysis with missing data and its extension to incomplete shapes. A well-known data set is used to illustrate the methodologies developed. The proposed methodology is applied to an apparel design problem in children.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0297-7
      Issue No: Vol. 12, No. 3 (2018)
       
  • Tree-structured modelling of categorical predictors in generalized
           additive regression
    • Authors: Gerhard Tutz; Moritz Berger
      Pages: 737 - 758
      Abstract: Generalized linear and additive models are very efficient regression tools but many parameters have to be estimated if categorical predictors with many categories are included. The method proposed here focusses on the main effects of categorical predictors by using tree type methods to obtain clusters of categories. When the predictor has many categories one wants to know in particular which of the categories have to be distinguished with respect to their effect on the response. The tree-structured approach allows to detect clusters of categories that share the same effect while letting other predictors, in particular metric predictors, have a linear or additive effect on the response. An algorithm for the fitting is proposed and various stopping criteria are evaluated. The preferred stopping criterion is based on p values representing a conditional inference procedure. In addition, stability of clusters is investigated and the relevance of predictors is investigated by bootstrap methods. Several applications show the usefulness of the tree-structured approach and small simulation studies demonstrate that the fitting procedure works well.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0298-6
      Issue No: Vol. 12, No. 3 (2018)
       
  • Outlier detection in interval data
    • Authors: A. Pedro Duarte Silva; Peter Filzmoser; Paula Brito
      Pages: 785 - 822
      Abstract: A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.
      PubDate: 2018-09-01
      DOI: 10.1007/s11634-017-0305-y
      Issue No: Vol. 12, No. 3 (2018)
       
  • Random effects clustering in multilevel modeling: choosing a proper
           partition
    • Authors: Claudio Conversano; Massimo Cannas; Francesco Mola; Emiliano Sironi
      Abstract: A novel criterion for estimating a latent partition of the observed groups based on the output of a hierarchical model is presented. It is based on a loss function combining the Gini income inequality ratio and the predictability index of Goodman and Kruskal in order to achieve maximum heterogeneity of random effects across groups and maximum homogeneity of predicted probabilities inside estimated clusters. The index is compared with alternative approaches in a simulation study and applied in a case study concerning the role of hospital level variables in deciding for a cesarean section.
      PubDate: 2018-10-12
      DOI: 10.1007/s11634-018-0347-9
       
  • Supervised learning via smoothed Polya trees
    • Authors: William Cipolli; Timothy Hanson
      Abstract: We propose a generative classification model that extends Quadratic Discriminant Analysis (QDA) (Cox in J R Stat Soc Ser B (Methodol) 20:215–242, 1958) and Linear Discriminant Analysis (LDA) (Fisher in Ann Eugen 7:179–188, 1936; Rao in J R Stat Soc Ser B 10:159–203, 1948) to the Bayesian nonparametric setting, providing a competitor to MclustDA (Fraley and Raftery in Am Stat Assoc 97:611–631, 2002). This approach models the data distribution for each class using a multivariate Polya tree and realizes impressive results in simulations and real data analyses. The flexibility gained from further relaxing the distributional assumptions of QDA can greatly improve the ability to correctly classify new observations for models with severe deviations from parametric distributional assumptions, while still performing well when the assumptions hold. The proposed method is quite fast compared to other supervised classifiers and very simple to implement as there are no kernel tricks or initialization steps perhaps making it one of the more user-friendly approaches to supervised learning. This highlights a significant feature of the proposed methodology as suboptimal tuning can greatly hamper classification performance; e.g., SVMs fit with non-optimal kernels perform significantly worse.
      PubDate: 2018-10-12
      DOI: 10.1007/s11634-018-0344-z
       
  • sARI: a soft agreement measure for class partitions incorporating
           assignment probabilities
    • Authors: Abby Flynt; Nema Dean; Rebecca Nugent
      Abstract: Agreement indices are commonly used to summarize the performance of both classification and clustering methods. The easy interpretation/intuition and desirable properties that result from the Rand and adjusted Rand indices, has led to their popularity over other available indices. While more algorithmic clustering approaches like k-means and hierarchical clustering produce hard partition assignments (assigning observations to a single cluster), other techniques like model-based clustering include information about the certainty of allocation of objects through class membership probabilities (soft partitions). To assess performance using traditional indices, e.g., the adjusted Rand index (ARI), the soft partition is mapped to a hard set of assignments, which commonly overstates the certainty of correct assignments. This paper proposes an extension of the ARI, the soft adjusted Rand index (sARI), with similar intuition and interpretation but also incorporating information from one or two soft partitions. It can be used in conjunction with the ARI, comparing the similarities of hard to soft, or soft to soft partitions to the similarities of the mapped hard partitions. Simulation study results support the intuition that in general, mapping to hard partitions tends to increase the measure of similarity between partitions. In applications, the sARI more accurately reflects the cluster boundary overlap commonly seen in real data.
      PubDate: 2018-10-09
      DOI: 10.1007/s11634-018-0346-x
       
  • Generalised linear model trees with global additive effects
    • Authors: Heidi Seibold; Torsten Hothorn; Achim Zeileis
      Abstract: Model-based trees are used to find subgroups in data which differ with respect to model parameters. In some applications it is natural to keep some parameters fixed globally for all observations while asking if and how other parameters vary across subgroups. Existing implementations of model-based trees can only deal with the scenario where all parameters depend on the subgroups. We propose partially additive linear model trees (PALM trees) as an extension of (generalised) linear model trees (LM and GLM trees, respectively), in which the model parameters are specified a priori to be estimated either globally from all observations or locally from the observations within the subgroups determined by the tree. Simulations show that the method has high power for detecting subgroups in the presence of global effects and reliably recovers the true parameters. Furthermore, treatment–subgroup differences are detected in an empirical application of the method to data from a mathematics exam: the PALM tree is able to detect a small subgroup of students that had a disadvantage in an exam with two versions while adjusting for overall ability effects.
      PubDate: 2018-10-05
      DOI: 10.1007/s11634-018-0342-1
       
  • A classification tree approach for the modeling of competing risks in
           discrete time
    • Authors: Moritz Berger; Thomas Welchowski; Steffen Schmitz-Valckenberg; Matthias Schmid
      Abstract: Cause-specific hazard models are a popular tool for the analysis of competing risks data. The classical modeling approach in discrete time consists of fitting parametric multinomial logit models. A drawback of this method is that the focus is on main effects only, and that higher order interactions are hard to handle. Moreover, the resulting models contain a large number of parameters, which may cause numerical problems when estimating coefficients. To overcome these problems, a tree-based model is proposed that extends the survival tree methodology developed previously for time-to-event models with one single type of event. The performance of the method, compared with several competitors, is investigated in simulations. The usefulness of the proposed approach is demonstrated by an analysis of age-related macular degeneration among elderly people that were monitored by annual study visits.
      PubDate: 2018-09-28
      DOI: 10.1007/s11634-018-0345-y
       
  • Variable selection in discriminant analysis for mixed continuous-binary
           variables and several groups
    • Authors: Alban Mbina Mbina; Guy Martial Nkiet; Fulgence Eyi Obiang
      Abstract: We propose a method for variable selection in discriminant analysis with mixed continuous and binary variables. This method is based on a criterion that permits to reduce the variable selection problem to a problem of estimating suitable permutation and dimensionality. Then, estimators for these parameters are proposed and the resulting method for selecting variables is shown to be consistent. A simulation study that permits to study several properties of the proposed approach and to compare it with an existing method is given, and an example on a real data set is provided.
      PubDate: 2018-09-21
      DOI: 10.1007/s11634-018-0343-0
       
  • Bayesian nonstationary Gaussian process models via treed process
           convolutions
    • Abstract: The Gaussian process is a common model in a wide variety of applications, such as environmental modeling, computer experiments, and geology. Two major challenges often arise: First, assuming that the process of interest is stationary over the entire domain often proves to be untenable. Second, the traditional Gaussian process model formulation is computationally inefficient for large datasets. In this paper, we propose a new Gaussian process model to tackle these problems based on the convolution of a smoothing kernel with a partitioned latent process. Nonstationarity can be modeled by allowing a separate latent process for each partition, which approximates a regional clustering structure. Partitioning follows a binary tree generating process similar to that of Classification and Regression Trees. A Bayesian approach is used to estimate the partitioning structure and model parameters simultaneously. Our motivating dataset consists of 11918 precipitation anomalies. Results show that our model has promising prediction performance and is computationally efficient for large datasets.
      PubDate: 2018-09-15
      DOI: 10.1007/s11634-018-0341-2
       
  • Finite mixtures, projection pursuit and tensor rank: a triangulation
    • Authors: Nicola Loperfido
      Abstract: Finite mixtures of multivariate distributions play a fundamental role in model-based clustering. However, they pose several problems, especially in the presence of many irrelevant variables. Dimension reduction methods, such as projection pursuit, are commonly used to address these problems. In this paper, we use skewness-maximizing projections to recover the subspace which optimally separates the cluster means. Skewness might then be removed in order to search for other potentially interesting data structures or to perform skewness-sensitive statistical analyses, such as the Hotelling’s \( T^{2}\) test. Our approach is algebraic in nature and deals with the symmetric tensor rank of the third multivariate cumulant. We also derive closed-form expressions for the symmetric tensor rank of the third cumulants of several multivariate mixture models, including mixtures of skew-normal distributions and mixtures of two symmetric components with proportional covariance matrices. Theoretical results in this paper shed some light on the connection between the estimated number of mixture components and their skewness.
      PubDate: 2018-09-06
      DOI: 10.1007/s11634-018-0336-z
       
 
 
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