Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Text document clustering based on frequent word sequences
Proceedings of the 14th ACM international conference on Information and knowledge management
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This paper presents a new clustering technique which is extended from the technique of clustering based on frequent-itemsets. Clustering based on frequent-itemsets has been used only in the domain of text documents and it does not consider frequency levels, which are the different levels of frequency of items in a data set. Our approach considers frequency levels together with frequent-itemsets. This new technique was applied in the domain of bio-informatics, specifically to obtain clusters of genes of zebrafish (Danio rerio) based on Expressed Sequence Tags (EST) that make up the genes. Since a particular EST is typically associated with only one gene, ESTs were first classified in to a set of classes based on their features. Then these EST classes were used in clustering genes. Further, an attempt was made to verify the quality of the clusters using gene ontology data. This paper presents the results of this application of clustering based on frequent-itemsets and frequency levels and discusses other domains in which it has potential uses.