Summarization – compressing data into an informative representation
Knowledge and Information Systems
Succinct summarization of transactional databases: an overlapped hyperrectangle scheme
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Bipartite Graph Framework for Summarizing High-Dimensional Binary, Categorical and Numeric Data
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
Time sequence summarization to scale up chronology-dependent applications
Proceedings of the 18th ACM conference on Information and knowledge management
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Summarizing transactional databases with overlapped hyperrectangles
Data Mining and Knowledge Discovery
Model order selection for boolean matrix factorization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A cluster centers initialization method for clustering categorical data
Expert Systems with Applications: An International Journal
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
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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 application to transaction or document 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 cluster the categorical data. By exploring key 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 show 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 effective in clustering the categorical data and summarizing the dense transaction databases.