A scalable algorithm for clustering sequential data

Valerie Guralnik, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

75 Scopus citations


In recent years, we have seen an enormous growth in the amount of available commercial and scientific data. Data from domains such as protein sequences, retail transactions, intrusion detection, and web-logs have an inherent sequential nature. Clustering of such data sets is useful for various purposes. For example, clustering of sequences from commercial data sets may help marketer identify different customer groups based upon their purchasing patterns. Grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. Over the years, many methods have been developed for clustering objects according to their similarity. However these methods tend to have a computational complexity that is at least quadratic on the number of sequences. In this paper we present an entirely different approach to sequence clustering that does not require an all-against-all analysis and uses a near-linear complexiy K -means based clustering algorithm. Our experiments using data sets derived from sequences of purchasing transactions and protein sequences show that this approach is scalable and leads to reasonably good clusters.

Original languageEnglish (US)
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Number of pages8
StatePublished - 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: Nov 29 2001Dec 2 2001

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other1st IEEE International Conference on Data Mining, ICDM'01
Country/TerritoryUnited States
CitySan Jose, CA


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