ACM Computing Surveys (CSUR)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Self-Adaptive Genetic Algorithm for Clustering
Journal of Heuristics
FGKA: a Fast Genetic K-means Clustering Algorithm
Proceedings of the 2004 ACM symposium on Applied computing
Evolutionary Algorithms for Clustering Gene-Expression Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
The Projection Explorer: A Flexible Tool for Projection-based Multidimensional Visualization
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Top 10 algorithms in data mining
Knowledge and Information Systems
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Cluster Analysis
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
Elementary Statistics Using Excel
Elementary Statistics Using Excel
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The automatic creation of literature abstracts
IBM Journal of Research and Development
Evolutionary genetic algorithm for efficient clustering of wireless sensor networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Evolutionary clustering of relational data
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Immunodomaince based Clonal Selection Clustering Algorithm
Applied Soft Computing
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
Applied Soft Computing
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Novelty detection algorithm for data streams multi-class problems
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
Evolutionary k-means for distributed data sets
Neurocomputing
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Abstract: One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets.