Finding Representative Set from Massive Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CONTOUR: an efficient algorithm for discovering discriminating subsequences
Data Mining and Knowledge Discovery
Hierarchical document clustering using local patterns
Data Mining and Knowledge Discovery
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Summarizing frequent patterns using profiles
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
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Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its applicationto the transaction (or document) classification and clustering. However, most of the frequent-itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to identify a subset of the most promising ones that can be used for clustering. In this paper, we study how to directly find a subset of high quality frequent itemsets that can be used as a concise summary of the transaction database and to clusterthe categorical data. By exploring some properties of the subset of itemsets that we are interested in, we proposed several search space pruning methods and designed an efficient algorithm called SUMMARY. Our empirical results have shown that SUMMARY runs very fast even when the minimum support is extremely low and scales very well with respect to the database size, and surprisingly, as a pure frequent itemset mining algorithm it is very effectivein clustering the categorical data and smmarizing the dense transaction databases.