Efficient and Robust Clustering on Large-scale Data Sets Using Fuzzy Neighborhood Functions

Hao Liu; Satoshi Oyama; Masahito Kurihara; Haruhiko Sato
March 2013
Proceedings of the International MultiConference of Engineers & ;2013, p1
Conference Proceeding
Density-based clustering algorithms are applied for the detection of clusters in spatial data sets, but typical algorithms usually have difficulties in selecting appropriate parameters. Recently, the FN-DBSCAN algorithm extended the density-based clustering algorithms with fuzzy set theory and solved this problem. However, FN-DBSCAN has a time complexity of 0(n2), which indicates that it is not suitable to deal with large-scale data sets. In this paper, we propose a novel clustering algorithm called landmark FN-DBSCAN which ensures linear time and space complexity with respect to the size of the input data set and empirically provides good clustering qualities.


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