Algorithms for clustering data
Algorithms for clustering data
Genetic algorithms for optimal image enhancement
Pattern Recognition Letters
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unsupervised Image Classification with a Hierarchical EM Algorithm
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
Robust information-theoretic clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
ITCH: information-theoretic cluster hierarchies
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
An information-theoretic external cluster-validity measure
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Hierarchical clustering algorithms have been studied extensively in the last years. However, existing approaches for hierarchical clustering suffer from several drawbacks. The representation of the results is often hard to interpret even for large datasets. Many approaches are not robust to noise objects or overcome these limitation only by difficult parameter settings. As many approaches heavily depend on their initialization, the resulting hierarchical clustering get stuck in a local optimum. In this paper, we propose the novel geneticbased hierarchical clustering algorithm GACH (Genetic Algorithm for finding Cluster Hierarchies) that solves those problems by a beneficial combination of genetic algorithms, information theory and model-based clustering. GACH is capable to find the correct number of model parameters using the Minimum Description Length (MDL) principle and does not depend on the initialization by the use of a population-based stochastic search which ensures a thorough exploration of the search space. Moreover, outliers are handled as they are assigned to appropriate inner nodes of the hierarchy or even to the root. An extensive evaluation of GACH on synthetic as well as on real data demonstrates the superiority of our algorithm over several existing approaches.