An Heterogeneous Population-Based Genetic Algorithm for Data Clustering
| Autor: | Amina Bedboudi, Mohamed Tahar Kimour, Cherif Bouras |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer Networks and Communications Computer science Correlation clustering Single-linkage clustering Constrained clustering 02 engineering and technology computer.software_genre Determining the number of clusters in a data set 020901 industrial engineering & automation Artificial Intelligence Hardware and Architecture Control and Systems Engineering CURE data clustering algorithm 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Canopy clustering algorithm Affinity propagation 020201 artificial intelligence & image processing Data mining Electrical and Electronic Engineering Cluster analysis computer Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 5 |
| ISSN: | 2089-3272 |
| Popis: | As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches. |
| Databáze: | OpenAIRE |
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