Clustering Based On Association Rule Hypergraphs

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. These discovered clusters are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related items using association rules, and clustering related transactions using clusters of items. Our approach is linearly scalable with respect to the number of transactions. The frequent item-sets used to derive association rules are also used to group items into a hypergraph edge, and a hypergraph partitioning algorithm is used to nd the clusters. Our experiments indicate that clustering using association rule hypergraphs holds great promise in several application domains. Our experiments with stock-market data and congressional voting data show that this clustering scheme is able to successfully group items that belong to the same group. Clustering of items can a...

• Type: Conference
• Year: 1997
• Where: DMKD
• Authors: Eui-Hong Han, George Karypis, Vipin Kumar, Bamshad Mobasher
• Keywords: Association Rule, Congressional Voting Data, Cray Research Inc, Data Mining, DMKD 1997