Density-ratio based clustering for discovering clusters with varying densities

Ye Zhu, Kai Ming Ting, Mark J. Carman

    Research output: Contribution to journalArticleResearchpeer-review

    61 Citations (Scopus)

    Abstract

    Density-based clustering algorithms are able to identify clusters of arbitrary shapes and sizes in a dataset which contains noise. It is well-known that most of these algorithms, which use a global density threshold, have difficulty identifying all clusters in a dataset having clusters of greatly varying densities. This paper identifies and analyses the condition under which density-based clustering algorithms fail in this scenario. It proposes a density-ratio based method to overcome this weakness, and reveals that it can be implemented in two approaches. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. We provide an empirical evaluation using DBSCAN, OPTICS and SNN to show the effectiveness of these two approaches.

    Original languageEnglish
    Pages (from-to)983-997
    Number of pages15
    JournalPattern Recognition
    Volume60
    DOIs
    Publication statusPublished - 1 Dec 2016

    Keywords

    • Density-based clustering
    • Density-ratio
    • Scaling
    • Varying densities

    Cite this