Mining representative subset based on fuzzy clustering
Hongfang Zhou, Boqin Feng, LU Lin-tao · Wuhan University Journal of Natural Sciences · 2007
Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm—RSMA (representative subset mining algorithm) is proposed, which can abstract representative subset from massive data. To accelerate the speed of producing representative subset, an improved algorithm—ARSMA(accelerated representative subset mining algorithm) is advanced, which adopt combining putting forward with backward strategies. In this way, the performance of the algorithm is improved. Finally we make experiments on real datasets and evaluate the representative subset. The experiment shows that ARSMA algorithm is more excellent than RandomPick algorithm either on effectiveness or efficiency.