Data depth based clustering analysis

Jeong, Myeong -Hun; Cai, Yaping; Sullivan, Clair J.; Wang, Shaowen

doi:10.1145/2996913.2996984

Title: Data depth based clustering analysis

Conference · Fri Jan 01 00:00:00 EST 2016

DOI:https://doi.org/10.1145/2996913.2996984· OSTI ID:1438413

Jeong, Myeong -Hun ^[1]; Cai, Yaping ^[1]; Sullivan, Clair J. ^[1]; Wang, Shaowen ^[1]

Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)

Here, 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.

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Research Organization:: North Carolina State University, Raleigh, NC (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)

DOE Contract Number:: NA0002576

OSTI ID:: 1438413

Resource Relation:: Conference: 24. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Francisco, CA (United States), 31 Oct- 3 Nov 2016

Country of Publication:: United States

Language:: English

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