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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 83 - 93 Abstract: This research is an assessment of classification with neural network classifier (NNC) against various classifiers centred on working effectiveness of various classifiers. We have compared resultant factors of NBC with other two algorithms, namely ripple down rule learner (RIDOR) and simple cart in order to get comparatively efficient and accurate results. NBC performs well on categorical as well as on numerical data. Along these lines, we have proposed a model of a hybrid technology of analysing accuracy proportion of NNC with NBC, rule-based classifiers and tree-based classifiers on diagnosis of heart disease dataset. Algorithms which we have used here are NBC, ripple down rule learner (RIDOR) and simple cart. This work considered substantial dataset and distinctive methodology for the connected classifier work NBC calculation, is taken as base methodology and every methodology like RIDOR and simple cart are utilized for examination, so as to anticipate heart disease status of patients. Keywords: naive Bayes classifier; NBC; trees classifier; simple cart; rule classification; RIDOR; neural network classifier; NNC; hybrid methodology; classifier; data mining; heart disease Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 83 - 93 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111659 Issue No:Vol. 2, No. 2 (2020)
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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 94 - 107 Abstract: Recently a density peaks clustering algorithm (DPC) was proposed to obtain arbitrary shapes of the clusters effectively. The cluster centres are discovered by finding density peaks according to the decision graph which drawn based on the density-distance. However, the computational complexity is extremely high for calculating the density-distance of each point, which limits the application of DPC for the large-scale data sets. To overcome this limitation, an efficient density-based clustering algorithm with circle-filtering strategy (CFC) is proposed. CFC removes useless points based on a circle-filtering strategy first, and then the cluster centres are selected according to the remaining points. Experimental results show that CFC can reduce the computational complexity on the basis of ensuring the accuracy of clustering, and outperforms DPC. Keywords: density peaks clustering algorithm; circle-filtering strategy; CFC; large-scale data set; decision graph; computational complexity Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 94 - 107 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111660 Issue No:Vol. 2, No. 2 (2020)
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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 108 - 124 Abstract: For the time series with high acquisition frequency and high noise, it is difficult to establish the prediction model directly. If we simply take their average values, we will lose a lot of trend information. Therefore, we studied how to accurately obtain the trend information of the time series and establish its accurate prediction model, and proposed a prediction model based on K-means clustering. The first step of the model is to obtain the trend information of the original time series based on the K-means clustering idea, and the second step is to use the gated recurrent unit based on the attention mechanism to establish a prediction model for the trend information. Experiments on three dataset show that the proposed K-means clustering method can effectively reduce noise interference and accurately obtain trend information. Comparative experiments on different prediction models show that our proposed prediction model has the best prediction accuracy. Keywords: time series; change trend prediction; K-means clustering; attention mechanism; gated recurrent unit Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 108 - 124 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111665 Issue No:Vol. 2, No. 2 (2020)
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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 125 - 137 Abstract: Businesses revolve around customers. Companies require knowing and understanding their customers well. To achieve this, companies require knowing customer requirements and having customer insights. Customer insights cannot be generated unless companies have processes in place to collect marketing information and marketing intelligence. The focus is to do a detailed discussion about the various aspects involved in collection of marketing information and marketing intelligence for generating customer insights. Companies require having an efficient marketing information system in place. Marketing information systems help in capturing and storing information about customers. The paper also focuses on competitive intelligence to have a better understanding of customers. Companies require following ethical practices in collecting and analysing marketing intelligence. The paper focuses on this important aspect of business. Proper coordination and utilisation of marketing information, marketing intelligence, and marketing information systems will help companies to generate better customer insights and develop customer relationships. Keywords: marketing information systems; internal data; analysis of information; privacy issues; customer requirements; marketing research; qualitative technique; customer relationship Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 125 - 137 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111666 Issue No:Vol. 2, No. 2 (2020)
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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 138 - 145 Abstract: Under the deterministic deployment of the video sensor nodes, the nodes are fixed in position and adjustable in direction. In order to improve the coverage of video sensor networks, a multi-group directional sensing model is designed, and a coverage dynamic optimisation for video sensor networks based on particle swarm optimisation algorithm is proposed. The designed algorithm adopts cosine adaptive inertia weight, and adaptively adjusts it from large to small with the number of iterations. It can improve the coverage rate of networks by optimising the sensing model. The experimental results show that the proposed algorithm has better results of different sensing angles, and can improve coverage rate in the condition of deterministic deployment of nodes. Keywords: coverage rate; dynamic optimisation; video sensor networks; particle swarm optimisation; PSO Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 138 - 145 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111667 Issue No:Vol. 2, No. 2 (2020)
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Authors:Aakanksha Jain, Abhishek Kumar, Jyotir Moy Chatterjee, Pramod Singh Rathore Pages: 146 - 157 Abstract: Multi-view subspace clustering is a type of subspace clustering which combines with multi-view learning. It cannot only deal with the challenges of the big data and high dimensions, but also cluster multi-view data from multiple sources and observation angles according to some similarity measurement. Our paper is to introduce the theoretical basis and latest research progress of multi-view subspace clustering. First, we briefly describe the basic principles, research status and classification of subspace clustering, and also compare several kinds of subspace clustering algorithms; next, multi-view subspace clustering is described in detail, and the ideas of several clustering algorithms are analysed and summarised. After that, we elaborate the research status of multi-view subspace clustering. Finally, the application of multi-view subspace clustering in various fields' application is introduced. The purpose of this paper is for beginners to quickly know about the research status of multi-view subspace clustering and some ideas of typical algorithms. Keywords: multi-view subspace clustering; subspace clustering; data mining; self-representation; spectral clustering-based methods Citation: International Journal of Collaborative Intelligence, Vol. 2, No. 2 (2020) pp. 146 - 157 PubDate: 2020-12-08T23:20:50-05:00 DOI: 10.1504/IJCI.2020.111671 Issue No:Vol. 2, No. 2 (2020)
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