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Publisher: Springer-Verlag (Total: 2352 journals)

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Showing 1 - 200 of 2352 Journals sorted alphabetically
3D Research     Hybrid Journal   (Followers: 18, SJR: 0.214, h-index: 10)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 9, SJR: 1.073, h-index: 25)
AAPS J.     Hybrid Journal   (Followers: 20, SJR: 1.192, h-index: 74)
AAPS PharmSciTech     Hybrid Journal   (Followers: 6, SJR: 0.718, h-index: 54)
Abdominal Imaging     Hybrid Journal   (Followers: 14, SJR: 0.723, h-index: 60)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 3, SJR: 0.447, h-index: 12)
Academic Psychiatry     Full-text available via subscription   (Followers: 22, SJR: 0.492, h-index: 32)
Academic Questions     Hybrid Journal   (Followers: 7, SJR: 0.135, h-index: 6)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 26, SJR: 0.378, h-index: 30)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.355, h-index: 20)
Acoustics Australia     Hybrid Journal  
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.387, h-index: 6)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.624, h-index: 34)
Acta Biotheoretica     Hybrid Journal   (Followers: 5, SJR: 0.419, h-index: 25)
Acta Diabetologica     Hybrid Journal   (Followers: 14, SJR: 1.318, h-index: 46)
Acta Endoscopica     Hybrid Journal   (Followers: 1, SJR: 0.113, h-index: 8)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.465, h-index: 23)
Acta Geochimica     Hybrid Journal   (Followers: 5)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 1, SJR: 0.294, h-index: 13)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.818, h-index: 22)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.524, h-index: 32)
Acta Mathematica     Hybrid Journal   (Followers: 11, SJR: 8.021, h-index: 47)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.53, h-index: 29)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5, SJR: 0.406, h-index: 30)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.451, h-index: 5)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.22, h-index: 20)
Acta Mechanica     Hybrid Journal   (Followers: 20, SJR: 0.898, h-index: 52)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.426, h-index: 29)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5, SJR: 0.525, h-index: 18)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.524, h-index: 14)
Acta Neurochirurgica     Hybrid Journal   (Followers: 6, SJR: 0.833, h-index: 73)
Acta Neurologica Belgica     Hybrid Journal   (SJR: 0.348, h-index: 27)
Acta Neuropathologica     Hybrid Journal   (Followers: 5, SJR: 6.61, h-index: 117)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.295, h-index: 17)
Acta Parasitologica     Hybrid Journal   (Followers: 9, SJR: 0.581, h-index: 28)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 2, SJR: 0.551, h-index: 39)
Acta Politica     Hybrid Journal   (Followers: 13, SJR: 0.658, h-index: 20)
Activitas Nervosa Superior     Hybrid Journal  
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 5, SJR: 0.103, h-index: 4)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 23, SJR: 0.871, h-index: 15)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 8)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 15, SJR: 0.795, h-index: 40)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.774, h-index: 52)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3, SJR: 0.319, h-index: 15)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 34, SJR: 0.959, h-index: 44)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15, SJR: 1.255, h-index: 44)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52, SJR: 1.113, h-index: 14)
Advances in Gerontology     Partially Free   (Followers: 9, SJR: 0.141, h-index: 3)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 22, SJR: 1.397, h-index: 42)
Advances in Manufacturing     Hybrid Journal   (Followers: 3, SJR: 0.2, h-index: 4)
Advances in Polymer Science     Hybrid Journal   (Followers: 41, SJR: 0.637, h-index: 89)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 0.79, h-index: 44)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 7)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.882, h-index: 23)
Aerobiologia     Hybrid Journal   (Followers: 1, SJR: 0.511, h-index: 36)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 9, SJR: 0.821, h-index: 49)
African Archaeological Review     Hybrid Journal   (Followers: 15, SJR: 0.612, h-index: 24)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.248, h-index: 6)
AGE     Hybrid Journal   (Followers: 7, SJR: 1.358, h-index: 33)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.337, h-index: 10)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.529, h-index: 55)
Agricultural Research     Hybrid Journal   (Followers: 3)
Agriculture and Human Values     Hybrid Journal   (Followers: 12, SJR: 1.197, h-index: 49)
Agroforestry Systems     Hybrid Journal   (Followers: 19, SJR: 0.64, h-index: 56)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 10, SJR: 1.732, h-index: 59)
AI & Society     Hybrid Journal   (Followers: 7, SJR: 0.171, h-index: 19)
AIDS and Behavior     Hybrid Journal   (Followers: 13, SJR: 2.006, h-index: 71)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.706, h-index: 19)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 4, SJR: 0.566, h-index: 18)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.388, h-index: 22)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 0.868, h-index: 20)
Algorithmica     Hybrid Journal   (Followers: 8, SJR: 0.898, h-index: 56)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.183, h-index: 20)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 0.729, h-index: 20)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3, SJR: 1.392, h-index: 32)
AMBIO     Hybrid Journal   (Followers: 15, SJR: 1.094, h-index: 87)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 15, SJR: 0.864, h-index: 39)
American J. of Community Psychology     Hybrid Journal   (Followers: 24, SJR: 1.237, h-index: 83)
American J. of Criminal Justice     Hybrid Journal   (Followers: 6, SJR: 0.634, h-index: 13)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 12, SJR: 0.283, h-index: 3)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.175, h-index: 13)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.558, h-index: 35)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 22, SJR: 0.293, h-index: 13)
American Sociologist     Hybrid Journal   (Followers: 14, SJR: 0.18, h-index: 13)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.362, h-index: 83)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.21, h-index: 37)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 3, SJR: 0.665, h-index: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 5)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 30, SJR: 1.096, h-index: 123)
Anatomical Science Intl.     Hybrid Journal   (Followers: 2, SJR: 0.301, h-index: 26)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.212, h-index: 69)
Animal Cognition     Hybrid Journal   (Followers: 16, SJR: 1.122, h-index: 55)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.156, h-index: 4)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.377, h-index: 32)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.504, h-index: 14)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.167, h-index: 26)
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 11, SJR: 2.112, h-index: 98)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 18, SJR: 1.182, h-index: 94)
Annals of Combinatorics     Hybrid Journal   (Followers: 3, SJR: 0.849, h-index: 15)
Annals of Data Science     Hybrid Journal   (Followers: 9)
Annals of Dyslexia     Hybrid Journal   (Followers: 9, SJR: 0.857, h-index: 40)
Annals of Finance     Hybrid Journal   (Followers: 28, SJR: 0.686, h-index: 14)
Annals of Forest Science     Hybrid Journal   (Followers: 6, SJR: 0.929, h-index: 57)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.136, h-index: 23)
Annals of Hematology     Hybrid Journal   (Followers: 14, SJR: 1.117, h-index: 62)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6, SJR: 0.593, h-index: 42)
Annals of Microbiology     Hybrid Journal   (Followers: 10, SJR: 0.402, h-index: 26)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.68, h-index: 45)
Annals of Operations Research     Hybrid Journal   (Followers: 8, SJR: 1.186, h-index: 78)
Annals of Ophthalmology     Hybrid Journal   (Followers: 11)
Annals of Regional Science     Hybrid Journal   (Followers: 7, SJR: 0.405, h-index: 42)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 10, SJR: 0.553, h-index: 8)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 15, SJR: 1.902, h-index: 127)
Annals of Telecommunications     Hybrid Journal   (Followers: 7, SJR: 0.315, h-index: 25)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.931, h-index: 31)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.992, h-index: 87)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.14, h-index: 57)
APOPTOSIS     Hybrid Journal   (Followers: 8, SJR: 1.554, h-index: 87)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.354, h-index: 27)
Applications of Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.274, h-index: 20)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.575, h-index: 80)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 17, SJR: 0.267, h-index: 26)
Applied Cancer Research     Open Access  
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.361, h-index: 21)
Applied Composite Materials     Hybrid Journal   (Followers: 48, SJR: 0.705, h-index: 35)
Applied Entomology and Zoology     Partially Free   (Followers: 3, SJR: 0.554, h-index: 34)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.323, h-index: 9)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.541, h-index: 13)
Applied Intelligence     Hybrid Journal   (Followers: 11, SJR: 0.777, h-index: 43)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.358, h-index: 34)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4, SJR: 0.955, h-index: 33)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.275, h-index: 8)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.37, h-index: 26)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 62, SJR: 1.262, h-index: 161)
Applied Physics A     Hybrid Journal   (Followers: 7, SJR: 0.535, h-index: 121)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 23, SJR: 0.983, h-index: 104)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.677, h-index: 47)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 11, SJR: 0.288, h-index: 15)
Applied Solar Energy     Hybrid Journal   (Followers: 16, SJR: 0.251, h-index: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4, SJR: 0.351, h-index: 9)
Aquaculture Intl.     Hybrid Journal   (Followers: 22, SJR: 0.613, h-index: 40)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 1)
Aquatic Ecology     Hybrid Journal   (Followers: 30, SJR: 0.646, h-index: 44)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.764, h-index: 39)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.172, h-index: 53)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.345, h-index: 20)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 1, SJR: 0.417, h-index: 16)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 22, SJR: 1.056, h-index: 15)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.397, h-index: 13)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.597, h-index: 29)
Archival Science     Hybrid Journal   (Followers: 54, SJR: 0.804, h-index: 22)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.28, h-index: 15)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1, SJR: 0.946, h-index: 23)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 4.091, h-index: 66)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.865, h-index: 40)
Archives and Museum Informatics     Hybrid Journal   (Followers: 129)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4, SJR: 2.841, h-index: 40)
Archives of Dermatological Research     Hybrid Journal   (Followers: 6, SJR: 0.9, h-index: 65)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 10, SJR: 0.846, h-index: 84)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 17, SJR: 0.695, h-index: 47)
Archives of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.702, h-index: 85)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 8, SJR: 1.039, h-index: 56)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 1.092, h-index: 13)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 9, SJR: 1.198, h-index: 74)
Archives of Toxicology     Hybrid Journal   (Followers: 16, SJR: 1.595, h-index: 76)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 1.086, h-index: 90)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 14, SJR: 1.264, h-index: 50)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 1.2, h-index: 42)
ArgoSpine News & J.     Hybrid Journal   (SJR: 0.102, h-index: 3)
Argumentation     Hybrid Journal   (Followers: 5, SJR: 0.295, h-index: 18)
Arid Ecosystems     Hybrid Journal   (Followers: 3)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.948, h-index: 22)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.797, h-index: 17)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.145, h-index: 8)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 10, SJR: 0.288, h-index: 25)
Artificial Intelligence Review     Hybrid Journal   (Followers: 14, SJR: 0.948, h-index: 48)
Artificial Life and Robotics     Hybrid Journal   (Followers: 8, SJR: 0.231, h-index: 14)
Asia Europe J.     Hybrid Journal   (Followers: 4, SJR: 0.247, h-index: 9)
Asia Pacific Education Review     Hybrid Journal   (Followers: 10, SJR: 0.371, h-index: 17)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 14, SJR: 1.676, h-index: 50)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 11, SJR: 0.353, h-index: 13)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.19, h-index: 15)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 20, SJR: 1.006, h-index: 14)
Asian Business & Management     Hybrid Journal   (Followers: 7, SJR: 0.41, h-index: 10)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 7)
Asian J. of Criminology     Hybrid Journal   (Followers: 5, SJR: 0.263, h-index: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2, SJR: 0.681, h-index: 15)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.195, h-index: 5)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  

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Journal Cover Advances in Data Analysis and Classification
  [SJR: 1.113]   [H-I: 14]   [52 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1862-5355 - ISSN (Online) 1862-5347
   Published by Springer-Verlag Homepage  [2352 journals]
  • Editorial for issue 3/2017
    • Pages: 441 - 444
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-017-0291-0
      Issue No: Vol. 11, No. 3 (2017)
       
  • Multivariate and functional classification using depth and distance
    • Authors: Mia Hubert; Peter Rousseeuw; Pieter Segaert
      Pages: 445 - 466
      Abstract: Abstract We construct classifiers for multivariate and functional data. Our approach is based on a kind of distance between data points and classes. The distance measure needs to be robust to outliers and invariant to linear transformations of the data. For this purpose we can use the bagdistance which is based on halfspace depth. It satisfies most of the properties of a norm but is able to reflect asymmetry when the class is skewed. Alternatively we can compute a measure of outlyingness based on the skew-adjusted projection depth. In either case we propose the DistSpace transform which maps each data point to the vector of its distances to all classes, followed by k-nearest neighbor (kNN) classification of the transformed data points. This combines invariance and robustness with the simplicity and wide applicability of kNN. The proposal is compared with other methods in experiments with real and simulated data.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0269-3
      Issue No: Vol. 11, No. 3 (2017)
       
  • Benchmarking different clustering algorithms on functional data
    • Authors: Christina Yassouridis; Friedrich Leisch
      Pages: 467 - 492
      Abstract: Abstract Theoretical knowledge of clustering functions is still scarce and only few models are available in form of applicable code. In literature, most methods are based on the projection of the functions onto a basis and building fixed or random effects models of the basis coefficients. They involve various parameters, among them number of basis functions, projection dimension, number of iterations etc. They usually work well on the data presented in the articles, but their performance has in most cases not been tested objectively on other data sets, nor against each other. The purpose of this paper is to give an overview of several existing methods to cluster functional data. An outline of their theoretic concepts is given and the meaning of their hyperparameters is explained. A simulation study was set up to analyze the parameters’ efficiency and sensitivity on different types of data sets, that were registered on regular and on irregular grids. For each method, a linear model of the clustering results was evaluated with different parameter levels as predictors. Later, the methods’ performances were compared to each other with the help of a visualization tool, to identify which method works the best on a specific kind of data.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0261-y
      Issue No: Vol. 11, No. 3 (2017)
       
  • Constrained clustering with a complex cluster structure
    • Authors: Marek Śmieja; Magdalena Wiercioch
      Pages: 493 - 518
      Abstract: Abstract In this contribution we present a novel constrained clustering method, Constrained clustering with a complex cluster structure (C4s), which incorporates equivalence constraints, both positive and negative, as the background information. C4s is capable of discovering groups of arbitrary structure, e.g. with multi-modal distribution, since at the initial stage the equivalence classes of elements generated by the positive constraints are split into smaller parts. This provides a detailed description of elements, which are in positive equivalence relation. In order to enable an automatic detection of the number of groups, the cross-entropy clustering is applied for each partitioning process. Experiments show that the proposed method achieves significantly better results than previous constrained clustering approaches. The advantage of our algorithm increases when we are focusing on finding partitions with complex structure of clusters.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0254-x
      Issue No: Vol. 11, No. 3 (2017)
       
  • A fuzzy neural network based framework to discover user access patterns
           from web log data
    • Authors: Zahid A. Ansari; Syed Abdul Sattar; A. Vinaya Babu
      Pages: 519 - 546
      Abstract: Abstract Clustering data from web user sessions is extensively applied to extract customer usage behavior to serve customized content to individual users. Due to the human involvement, web usage data usually contain noisy, incomplete and vague information. Neural networks have the capability to extract embedded knowledge in the form of user session clusters from the huge web usage data. Moreover, they provide tolerance against imperfect and noisy data. Fuzzy sets are another popular tool utilized for handling uncertainty and vagueness hidden in the data. In this paper a fuzzy neural clustering network (FNCN) based framework is proposed that makes use of the fuzzy membership concept of fuzzy c-means (FCM) clustering and the learning rate of a modified self-organizing map (MSOM) neural network model and tries to minimize the weighted sum of the squared error. FNCN is applied to cluster the users’ web access data extracted from the web logs of an educational institution’s proxy web server. The performance of FNCN is compared with FCM and MSOM based clustering methods using various validity indexes. Our results show that FNCN produces better quality of clusters than FCM and MSOM.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-015-0228-4
      Issue No: Vol. 11, No. 3 (2017)
       
  • Dense traffic flow patterns mining in bi-directional road networks using
           density based trajectory clustering
    • Authors: Vaishali Mirge; Kesari Verma; Shubhrata Gupta
      Pages: 547 - 561
      Abstract: Abstract Due to the rapid growth of wireless communications and positioning technologies, trajectory data have become increasingly popular, posing great challenges to the researchers of data mining and machine learning community. Trajectory data are obtained using GPS devices that capture the position of an object at specific time intervals. These enormous amounts of data necessitates to explore efficient and effective techniques to extract useful information to solve real world problems. Traffic flow pattern mining is one of the challenging issues for many applications. In a literature significant number of approaches are available to cluster the trajectory data, however the clustering has not been explored for trajectories pattern mining in bi-directional road networks. This paper presents a novel technique for excavating heavy traffic flow patterns in bi-directional road network, i.e. identifying divisions of the roads where the traffic flow is very dense. The proposed technique works in two phases: phase I, finds the clusters of trajectory points based on density of trajectory points; phase II, arranges the clusters in sequence based on spatiotemporal values for each route and directions. These sequences represent the traffic flow patterns. All the routes and sections exceeding a user specified minimum traffic threshold are marked as high dense traffic areas. The experiments are performed on synthetic dataset. The proposed algorithm efficiently and accurately finds the dense traffic in bi-directional roads. Proposed clustering method is compared with the standard k-means clustering algorithm for the performance evaluation.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0256-8
      Issue No: Vol. 11, No. 3 (2017)
       
  • Disjoint factor analysis with cross-loadings
    • Authors: Maurizio Vichi
      Pages: 563 - 591
      Abstract: Abstract Disjoint factor analysis (DFA) is a new latent factor model that we propose here to identify factors that relate to disjoint subsets of variables, thus simplifying the loading matrix structure. Similarly to exploratory factor analysis (EFA), the DFA does not hypothesize prior information on the number of factors and on the relevant relations between variables and factors. In DFA the population variance–covariance structure is hypothesized block diagonal after the proper permutation of variables and estimated by Maximum Likelihood, using an Coordinate Descent type algorithm. Inference on parameters on the number of factors and to confirm the hypothesized simple structure are provided. Properties such as scale equivariance, uniqueness, optimal simplification of loadings are satisfied by DFA. Relevant cross-loadings are also estimated in case they are detected from the best DFA solution. DFA has also the option to constrain a variable to load on a pre-specified factor so that the researcher can assume, a priori, some relations between variables and loadings. A simulation study shows performances of DFA and an application to optimally identify the dimensions of well-being is used to illustrate characteristics of the new methodology. A final discussion concludes the paper.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0263-9
      Issue No: Vol. 11, No. 3 (2017)
       
  • General location model with factor analyzer covariance matrix structure
           and its applications
    • Authors: Leila Amiri; Mojtaba Khazaei; Mojtaba Ganjali
      Pages: 593 - 609
      Abstract: Abstract General location model (GLOM) is a well-known model for analyzing mixed data. In GLOM one decomposes the joint distribution of variables into conditional distribution of continuous variables given categorical outcomes and marginal distribution of categorical variables. The first version of GLOM assumes that the covariance matrices of continuous multivariate distributions across cells, which are obtained by different combination of categorical variables, are equal. In this paper, the GLOMs are considered in both cases of equality and unequality of these covariance matrices. Three covariance structures are used across cells: the same factor analyzer, factor analyzer with unequal specific variances matrices (in the general and parsimonious forms) and factor analyzers with common factor loadings. These structures are used for both modeling covariance structure and for reducing the number of parameters. The maximum likelihood estimates of parameters are computed via the EM algorithm. As an application for these models, we investigate the classification of continuous variables within cells. Based on these models, the classification is done for usual as well as for high dimensional data sets. Finally, for showing the applicability of the proposed models for classification, results from analyzing three real data sets are presented.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0258-6
      Issue No: Vol. 11, No. 3 (2017)
       
  • Multi-objective retinal vessel localization using flower pollination
           search algorithm with pattern search
    • Authors: E. Emary; Hossam M. Zawbaa; Aboul Ella Hassanien; B. Parv
      Pages: 611 - 627
      Abstract: Abstract This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0257-7
      Issue No: Vol. 11, No. 3 (2017)
       
  • A new approach for determining the prior probabilities in the
           classification problem by Bayesian method
    • Authors: Thao Nguyen-Trang; Tai Vo-Van
      Pages: 629 - 643
      Abstract: Abstract In this article, we suggest a new algorithm to identify the prior probabilities for classification problem by Bayesian method. The prior probabilities are determined by combining the information of populations in training set and the new observations through fuzzy clustering method (FCM) instead of using uniform distribution or the ratio of sample or Laplace method as the existing ones. We next combine the determined prior probabilities and the estimated likelihood functions to classify the new object. In practice, calculations are performed by Matlab procedures. The proposed algorithm is tested by the three numerical examples including bench mark and real data sets. The results show that the new approach is reasonable and gives more efficient than existing ones.
      PubDate: 2017-09-01
      DOI: 10.1007/s11634-016-0253-y
      Issue No: Vol. 11, No. 3 (2017)
       
  • Model-based regression clustering for high-dimensional data: application
           to functional data
    • Authors: Emilie Devijver
      Pages: 243 - 279
      Abstract: Abstract Finite mixture regression models are useful for modeling the relationship between response and predictors arising from different subpopulations. In this article, we study high-dimensional predictors and high-dimensional response and propose two procedures to cluster observations according to the link between predictors and the response. To reduce the dimension, we propose to use the Lasso estimator, which takes into account the sparsity and a maximum likelihood estimator penalized by the rank, to take into account the matrix structure. To choose the number of components and the sparsity level, we construct a collection of models, varying those two parameters and we select a model among this collection with a non-asymptotic criterion. We extend these procedures to functional data, where predictors and responses are functions. For this purpose, we use a wavelet-based approach. For each situation, we provide algorithms and apply and evaluate our methods both on simulated and real datasets, to understand how they work in practice.
      PubDate: 2017-06-01
      DOI: 10.1007/s11634-016-0242-1
      Issue No: Vol. 11, No. 2 (2017)
       
  • Mixture models for ordinal responses to account for uncertainty of choice
    • Authors: Gerhard Tutz; Micha Schneider; Maria Iannario; Domenico Piccolo
      Pages: 281 - 305
      Abstract: Abstract In CUB models the uncertainty of choice is explicitly modelled as a Combination of discrete Uniform and shifted Binomial random variables. The basic concept to model the response as a mixture of a deliberate choice of a response category and an uncertainty component that is represented by a uniform distribution on the response categories is extended to a much wider class of models. The deliberate choice can in particular be determined by classical ordinal response models as the cumulative and adjacent categories model. Then one obtains the traditional and flexible models as special cases when the uncertainty component is irrelevant. It is shown that the effect of explanatory variables is underestimated if the uncertainty component is neglected in a cumulative type mixture model. Visualization tools for the effects of variables are proposed and the modelling strategies are evaluated by use of real data sets. It is demonstrated that the extended class of models frequently yields better fit than classical ordinal response models without an uncertainty component.
      PubDate: 2017-06-01
      DOI: 10.1007/s11634-016-0247-9
      Issue No: Vol. 11, No. 2 (2017)
       
  • T3C: improving a decision tree classification algorithm’s interval
           splits on continuous attributes
    • Authors: Panagiotis Tzirakis; Christos Tjortjis
      Pages: 353 - 370
      Abstract: Abstract This paper proposes, describes and evaluates T3C, a classification algorithm that builds decision trees of depth at most three, and results in high accuracy whilst keeping the size of the tree reasonably small. T3C is an improvement over algorithm T3 in the way it performs splits on continuous attributes. When run against publicly available data sets, T3C achieved lower generalisation error than T3 and the popular C4.5, and competitive results compared to Random Forest and Rotation Forest.
      PubDate: 2017-06-01
      DOI: 10.1007/s11634-016-0246-x
      Issue No: Vol. 11, No. 2 (2017)
       
  • ADCLUS and INDCLUS: analysis, experimentation, and meta-heuristic
           algorithm extensions
    • Authors: Stephen L. France; Wen Chen; Yumin Deng
      Pages: 371 - 393
      Abstract: Abstract The ADCLUS and INDCLUS models, along with associated fitting techniques, can be used to extract an overlapping clustering structure from similarity data. In this paper, we examine the scalability of these models. We test the SINDLCUS algorithm and an adapted version of the SYMPRES algorithm on medium size datasets and try to infer their scalability and the degree of the local optima problem as the problem size increases. We describe several meta-heuristic approaches to minimizing the INDCLUS and ADCLUS loss functions.
      PubDate: 2017-06-01
      DOI: 10.1007/s11634-016-0244-z
      Issue No: Vol. 11, No. 2 (2017)
       
  • Backtransformation: a new representation of data processing chains with a
           scalar decision function
    • Authors: Mario Michael Krell; Sirko Straube
      Pages: 415 - 439
      Abstract: Abstract Data processing often transforms a complex signal using a set of different preprocessing algorithms to a single value as the outcome of a final decision function. Still, it is challenging to understand and visualize the interplay between the algorithms performing this transformation. Especially when dimensionality reduction is used, the original data structure (e.g., spatio-temporal information) is hidden from subsequent algorithms. To tackle this problem, we introduce the backtransformation concept suggesting to look at the combination of algorithms as one transformation which maps the original input signal to a single value. Therefore, it takes the derivative of the final decision function and transforms it back through the previous processing steps via backward iteration and the chain rule. The resulting derivative of the composed decision function in the sample of interest represents the complete decision process. Using it for visualizations might improve the understanding of the process. Often, it is possible to construct a feasible processing chain with affine mappings which simplifies the calculation for the backtransformation and the interpretation of the result a lot. In this case, the affine backtransformation provides the complete parameterization of the processing chain. This article introduces the theory, provides implementation guidelines, and presents three application examples.
      PubDate: 2017-06-01
      DOI: 10.1007/s11634-015-0229-3
      Issue No: Vol. 11, No. 2 (2017)
       
  • Signal classification with a point process distance on the space of
           persistence diagrams
    • Authors: Andrew Marchese; Vasileios Maroulas
      Abstract: Abstract In this paper, we consider the problem of signal classification. First, the signal is translated into a persistence diagram through the use of delay-embedding and persistent homology. Endowing the data space of persistence diagrams with a metric from point processes, we show that it admits statistical structure in the form of Fréchet means and variances and a classification scheme is established. In contrast with the Wasserstein distance, this metric accounts for changes in small persistence and changes in cardinality. The classification results using this distance are benchmarked on both synthetic data and real acoustic signals and it is demonstrated that this classifier outperforms current signal classification techniques.
      PubDate: 2017-10-13
      DOI: 10.1007/s11634-017-0294-x
       
  • A divisive clustering method for functional data with special
           consideration of outliers
    • Authors: Ana Justel; Marcela Svarc
      Abstract: 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: 2017-08-11
      DOI: 10.1007/s11634-017-0290-1
       
  • 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
      Abstract: 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: 2017-07-04
      DOI: 10.1007/s11634-017-0289-7
       
  • Minimum distance method for directional data and outlier detection
    • Authors: Mercedes Fernandez Sau; Daniela Rodriguez
      Abstract: 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: 2017-06-02
      DOI: 10.1007/s11634-017-0287-9
       
  • Editorial for issue 2/2017
    • PubDate: 2017-05-17
      DOI: 10.1007/s11634-017-0288-8
       
 
 
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