ADJ-CABOSFV for High Dimensional Sparse Data Clustering

SEN WU, XIAONAN GAO, LU LIU

Abstract


The classic algorithm for high dimensional sparse data clustering, CABOSFV, cannot adjust the sets once generated, which leads to the final clustering result impacted by the preceding clustering result. This paper proposes ADJ-CABOSFV that can adjust the sets clustered by CABOSFV and the objects in the same set clustered by ADJ-CABOSFV are more similar without increasing the number of parameters. The experiments on UCI data sets show that ADJ-CABOSFV maintains superiority on high-dimensional sparse data of binary variables, and the clustering quality is better than the classic CABOSFV.


DOI
10.12783/dtem/apme2016/8736

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