Clustering Algorithm Based on Maximal Frequent Itemsets
Meiling Liu · Jisuanji gongcheng · 2009
The concept and properties of frequent itemsets are introduced. The maximal frequent itemsets is made as clustering basis. A new Clustering Algorithm Based on Maximal Frequent Itemsets(CABMFI) is proposed,which integrates the association analysis and clustering analysis. The relevance between data items is fully used,and need not input the clustering number. It is tested with several datasets. Experimental results show that,compared with traditional distance-based K-Means clustering algorithm,this algorithm can reduce the time cost of computing the distance of objects,improve the efficiency,and has better accuracy. The interpretability of clustering results is also well.