Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Dynamic clustering for time incremental data
Pattern Recognition Letters
A new non-iterative approach for clustering
Pattern Recognition Letters
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Pattern Recognition
Clustering Algorithms
Improving the Orthogonal Range Search k -Windows Algorithm
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases
Pattern Recognition Letters
To combine steady-state genetic algorithm and ensemble learning for data clustering
Pattern Recognition Letters
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
Data clustering by minimizing disconnectivity
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
Improving DBSCAN's execution time by using a pruning technique on bit vectors
Pattern Recognition Letters
The study of special encoding in genetic algorithms and a sufficient convergence condition of GAs
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Grouping genetic algorithm for data clustering
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Towards hierarchical clustering
CSR'07 Proceedings of the Second international conference on Computer Science: theory and applications
International Journal of Applied Evolutionary Computation
Selection of canonical images of travel attractions using image clustering and aesthetics analysis
International Journal of Computational Science and Engineering
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In this paper we have presented a new genetically guided algorithm for solving the clustering problem. The proposed Genetic Clustering Algorithm is basically a two-phase process. At the first phase the original data set is decomposed into a number of fragmented clusters in order to spread the GA search process at the latter phase over the entire space. At the second phase Hierarchical Cluster Merging Algorithm (HCMA) is used. The HCMA is an iterative genetic algorithm based approach that combines some of the fragmented clusters into complete k-cluster. The algorithm contains another component called Adjacent Cluster Checking Algorithm (ACCA). This technique is used for testing adjacency of two segmented clusters so that they can be merged into one cluster. The performance of the algorithm has been demonstrated on several data sets consisting of multiple clusters and it is compared with some well-known clustering methods.