Proximity multi-sphere support vector clustering

    Research output: Contribution to journalArticle

    9 Citations (Scopus)

    Abstract

    Support vector data description constructs an optimal hypersphere in feature space as a description of a data set. This hypersphere when mapped back to input space becomes a set of contours, and support vector clustering (SVC) employs these contours as cluster boundaries to detect clusters in the data set. However real-world data sets may have some distinctive distributions and hence a single hypersphere cannot be the best description. As a result, the set of contours in input space does not always detect all clusters in the data set. Another issue in SVC is that in some cases, it cannot preserve proximity notation which is crucial for cluster analysis, that is, two data points that are close to each other can be assigned to different clusters using cluster labelling method of SVC. To overcome these drawbacks, we propose Proximity Multi-sphere Support Vector Clustering which employs a set of hyperspheres to provide a better data description for data sets having distinctive distributions and a proximity graph to favour the proximity notation. Experimental results on different data sets are presented to evaluate the proposed clustering technique and compare it with SVC and other clustering techniques
    Original languageEnglish
    Pages (from-to)1309-1319
    Number of pages11
    JournalNeural Computing and Applications
    Volume22
    Issue number7-8
    DOIs
    Publication statusPublished - 2013

      Fingerprint

    Cite this