Abstract
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.
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© 2007 Springer Berlin Heidelberg
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Wimalasuriya, D.C., Ramachandran, S., Dou, D. (2007). Clustering Zebrafish Genes Based on Frequent-Itemsets and Frequency Levels. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_102
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DOI: https://doi.org/10.1007/978-3-540-71701-0_102
Publisher Name: Springer, Berlin, Heidelberg
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