Algorithms for clustering data
Algorithms for clustering data
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Introduction to statistical pattern recognition (2nd ed.)
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Genetic Algorithms and Grouping Problems
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Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
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PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Information preserving multi-objective feature selection for unsupervised learning
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Multiobjective Genetic Fuzzy Clustering of Categorical Attributes
ICIT '07 Proceedings of the 10th International Conference on Information Technology
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Expert Systems with Applications: An International Journal
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Information Sciences: an International Journal
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IEEE Transactions on Evolutionary Computation
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting the trade-off — the benefits of multiple objectives in data clustering
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
A Bayesian imputation method for a clustering genetic algorithm
Journal of Computational Methods in Sciences and Engineering
A fuzzy co-clustering approach for hybrid recommender systems
International Journal of Hybrid Intelligent Systems
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In this paper, we propose a novel evolutionary clustering algorithm for mixed type data numerical and categorical. It is doing clustering and feature selection simultaneously. Feature subset selection improves quality of clustering. It also improves understandability and scalability. It unfastens attraction on numerical or categorical dataset only. K-prototype KP is a well-known partitional clustering algorithm for mixed type data. However, this type of algorithm is sensitive to initialization and may converge to local optima. It is optimizing a single measure only i.e. minimizations of intra cluster distance. We have considered clustering as a multi objective optimization problem MOOP. Minimization of intra cluster distance and maximization of inter cluster distance are two objectives of optimization. Multi objective genetic algorithm MOGA is a well-known algorithm which can be applicable for MOOP to find out near global optimal solution. So in this paper we have developed a hybridized genetic clustering algorithm by combining the global search ability of MOGA and local search ability of KP. Experiments on real-life benchmark datasets from UCI machine learning repository prove the superiority of the proposed algorithm.