NDLI: Data depth based clustering analysis

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Data depth based clustering analysis

Content Provider	ACM Digital Library
Author	Jeong, Myeong-Hun Sullivan, Clair J. Wang, Shaowen Cai, Yaping
Abstract	This paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2996984
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cluster analysis Density-based clustering analysis Affine invariant clustering Data depth
Content Type	Text
Resource Type	Article

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