关键词: 机器学习, 概念聚类, 关联聚类, 二部图, 二元组

Abstract: In traditional conceptual clustering algorithms，clusters updating and store depend not only on the number of objects and their attributes but also on the number of attribute values.This limitation results in that traditional conceptual clustering algorithms are not suitable for large data set.A new conceptual clustering algorithm based on bipartite graph called BGBCC is proposed，which clusters data together with their attributes effectively through obtaining approximate collection of maximum ε-bicliques.The experiments and analysis show that the algorithm can obtain good clustering descriptions，and can achieve conceptual clustering of large data set in sublinear time.

Key words: machine learning, conceptual clustering, conjunctive clustering, bipartite graph, biclique