A two-level clustering method using linear linkage encoding

Authors:
Emin Erkan Korkmaz
Affiliations:
Computer Engineering Department, Yeditepe University, İstanbul, Turkey
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

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Abstract

Linear Linkage Encoding (LLE) is a representational scheme proposed for Genetic Algorithms (GA). LLE is convenient to be used for grouping problems and it doesn't suffer from the redundancy problem that exists in classical encoding schemes. Any number of groups can be represented in a fixed length chromosome in this scheme. However, the length of the chromosome in LLE is determined by the number of elements to be grouped just like the other encoding schemes. This disadvantage becomes dominant when LLE is applied on large datasets and the encoding turns out to be an infeasible model. In this paper a two-level approach is proposed for LLE in order to overcome the problem. In this method, the large dataset is divided into a group of subsets. In the first phase of the process, the data in the subsets are grouped using LLE. Then these groups are used to obtain the final partitioning of the data in the second phase. The approach is tested on the clustering problem. Two considerably large datasets have been chosen for the experiments. It is not possible to obtain a satisfactory convergence with the straightforward application of LLE on these datasets. The method proposed can cluster the datasets with low error rates.