Algorithms for clustering data
Algorithms for clustering data
The design and analysis of spatial data structures
The design and analysis of spatial data structures
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Interactive Pattern Recognition
Interactive Pattern Recognition
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature-guided clustering of multi-dimensional flow cytometry datasets
Journal of Biomedical Informatics
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
Masseter segmentation using an improved watershed algorithm with unsupervised classification
Computers in Biology and Medicine
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Quantization-based clustering algorithm
Pattern Recognition
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
An algorithm for high-dimensional traffic data clustering
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A cluster centers initialization method for clustering categorical data
Expert Systems with Applications: An International Journal
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
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The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d\hbox{-}{\rm{dimensional}} space and the number of desired clusters k, k-means seeks a set of k cluster centers so as to minimize the sum of the squared Euclidean distance between each point and its nearest cluster center. However, the algorithm is very sensitive to the initial selection of centers and is likely to converge to partitions that are significantly inferior to the global optimum. We present a genetic algorithm (GA) for evolving centers in the k-means algorithm that simultaneously identifies good partitions for a range of values around a specified k. The set of centers is represented using a hyper-quadtree constructed on the data. This representation is exploited in our GA to generate an initial population of good centers and to support a novel crossover operation that selectively passes good subsets of neighboring centers from parents to offspring by swapping subtrees. Experimental results indicate that our GA finds the global optimum for data sets with known optima and finds good solutions for large simulated data sets.