Clonal selection algorithm for learning concept hierarchy from Malay text

Authors:
Mohd Zakree Ahmad Nazri;Siti Mariyam Shamsuddin;Azuraliza Abu Bakar
Affiliations:
Soft Computing Research Group, Fac. of Comp. Sci. and Inf. System, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia and Data Mining and Optimization Res. Group, Center for Artificial Intelli ...;Soft Computing Research Group, Faculty of Computer Science & Information System, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia;Data Mining and Optimization Research Group, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

Concept hierarchy is an integral part of ontology which is the backbone of the Semantic Web. This paper describes a new hierarchical clustering algorithm for learning concept hierarchy named Clonal Selection Algorithm for Learning Concept Hierarchy, or CLONACH. The proposed algorithm resembles the CLONALG. CLONACH's effectiveness is evaluated on three data sets. The results show that the concept hierarchy produced by CLONACH is better than the agglomerative clustering technique in terms of taxonomic overlaps. Thus, the CLONALG based algorithm has been regarded as a promising technique in learning from texts, in particular small collection of texts.