Mixed integer programming and heuristics approaches for clustering with cluster-based feature selection

In this study, we work on a clustering problem where it is assumed that the features identifying the clusters may differ for each cluster. Number of clusters and number of relevant features in each cluster are given in advance. A centerbased clustering approach is proposed. Finding the cluster centers, assigning the data points and selecting relevant features for each cluster are performed simultaneously. A non-linear mixed integer mathematical model is proposed which minimizes the total distance between data points and their cluster center by using the selected features for each cluster. Different linearization methods have been used for solving the problem. Besides, two different heuristic algorithms have been developed by taking into account the nature of the mentioned problem. Experimental results have been presented
Informs Annual Meeting , October 20-23 2019


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The probabilistic distance clustering method of the authors [2, 8], assumes the cluster membership probabilities given in terms of the distances of the data points from the cluster centers, and the cluster sizes. A resulting extremal principle is then used to update the cluster centers (as convex combinations of the data points), and the cluster sizes (if not given.) Progress is monitored by the joint distance function (JDF), a weighted harmonic mean of the above distances, that approximates the data by cap...
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Mixed integer programming and heuristics approachesfor clustering with cluster-based feature selection
Önen Öz, Sen; İyigün, Cem; Department of Industrial Engineering (2019)
Cluster analysis tries to figure out the hidden similarities between data points in orderto place similar data points into the same group and different data points into separategroups using unlabeled data. Understanding the data becomes difficult and the powerof obtaining informative clusters for an algorithm decreases as the dimensionality ofthe data set gets high. Identifying the relevant features of high dimensional data setsis the mostly used technique in order to increase the performance of the algorit...
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Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
Citation Formats
C. İyigün, “Mixed integer programming and heuristics approaches for clustering with cluster-based feature selection,” presented at the Informs Annual Meeting , October 20-23 2019, Washington, USA, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/86852.