Proceedings Article10.1109/FSKD.2009.215
Genetic Algorithm-Based High-dimensional Data Clustering Technique
Haojun Sun,Lang-huan Xiong +1 more
- 14 Aug 2009
- Vol. 1, pp 485-489
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TL;DR: A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, which searches feature subspace by genetic algorithms to find the effective clustering feature subspaces.
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Abstract: A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate features and cluster centers are binary encoded, and the degree of feature subspace contributes to subspace clustering is proposed as the fitness function. The experimental results indicate the feasibility and efficiency of the GA-HD clustering algorithm.
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TL;DR: An algorithmic framework for solving the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves, is developed and tested.
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A recommender system using GA K-means clustering in an online shopping market
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TL;DR: The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms and validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
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Genetic Algorithms: Theory and Applications
Ulrich Bodenhofer,Johannes Kepler +1 more
- 01 Jan 2002
TL;DR: Revised version of lectures notes of the lecture " Genetic Algorithms: Theory and Applications " held at the Johannes Kepler University, Linz, during the winter term 1999/2000.