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Fast support vector clustering

This fact impedes the application of support-based clustering method to the large-scale datasets. In this paper, we propose applying stochastic gradient descent framework to the first phase of support-based clustering for finding the domain of novelty in the form of a half-space and a new strategy to perform the clustering assignment. | Vietnam J Comput Sci 2017 4 13-21 DOI 10.1007 s4O595-016-0068-y CrossMark REGULAR PAPER Fast support vector clustering Tung Pham 1 Hang Dang1 Trung Le2 Thai Hoang Le1 Received 30 November 2015 Accepted 15 April 2016 Published online 12 May 2016 The Author s 2016. This article is published with open access at Springerlink.com Abstract Support-based clustering has recently absorbed plenty of attention because of its applications in solving the difficult and diverse clustering or outlier detection problem. Support-based clustering method perambulates two phases finding the domain of novelty and performing the clustering assignment. To find the domain of novelty the training time given by the current solvers is typically over-quadratic in the training size. This fact impedes the application of support-based clustering method to the large-scale datasets. In this paper we propose applying stochastic gradient descent framework to the first phase of support-based clustering for finding the domain of novelty in the form of a half-space and a new strategy to perform the clustering assignment. We validate our proposed method on several well-known datasets for clustering task to show that the proposed method renders a comparable clustering quality to the baselines while being faster than them. Keywords Support vector clustering Cluster analysis Kernel method 1 Introduction Cluster analysis is a fundamental problem in pattern recognition where objects are categorized into groups or clusters based on pairwise similarities between those objects such that two criteria homogeneity and separation are achieved B Hang Dang dthang@hcmus.edu.vn 1 Faculty of Information Technology VNUHCM-University of Science Ho Chi Minh City Vietnam 2 Faculty of Information Technology HCMc University of Pedagogy Ho Chi Minh City Vietnam 21 . Two challenges in the task of cluster analysis are 1 dealing with complicated data with nested or hierarchy structures inside and 2 automatically detecting the .