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Application of Genetic and Local Optimization Algorithms for Object Clustering Problem with Similarity Coefficients  

Yim, Dong-Soon (Department of Industrial and Systems Engineeringm Hannam University)
Oh, Hyun-Seung (Department of Industrial and Systems Engineeringm Hannam University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.29, no.1, 2003 , pp. 90-99 More about this Journal
Abstract
Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.
Keywords
object clustering; optimization; genetic algorithm; heuristic;
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