NDLI: Alternate biological sequence clustering using symbol table

Content Provider	IEEE Xplore Digital Library
Author	Baridam, B.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Port Harcourt, Port Harcourt, Nigeria (Baridam, B.)
Abstract	Clustering has to do with the identification of interesting distribution patterns and similarities, natural groupings or clusters, within a collection of objects in a dataset. Clustering is an unsupervised learning problem and can be distance-based or conceptual. In distance-based clustering the similarity criterion is based on distance. Objects belong to the same cluster if they are close according to a given distance. Conceptual clustering defines a concept common to all the objects in the cluster. In this case, objects are clustered based on their fitness to some descriptive concepts, and not according to distance or similarity measure. The extension of the usage of the common symbol table is employed in this paper to the clustering of biological sequences. The method does not depend on concept as does conceptual clustering rather it uses table (hash table or list). The result obtained indicates the usefulness of the symbol table in biological sequence clustering.
Starting Page	604
Ending Page	608
File Size	206969
Page Count	5
File Format	PDF
ISBN	9780989319300
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-07
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	The Science and Information Organization
Subject Keyword	Algorithm design and analysis Heuristic algorithms Clustering algorithms Gene expression Data mining Bioinformatics Biological information theory
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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