A New Version of the Price‘s Algorithm for Global Optimization
Journal of Global Optimization
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Genetic approach helps to speed classical Price algorithm for global optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering and visualization approaches for human cell cycle gene expression data analysis
International Journal of Approximate Reasoning
PCA Based Feature Selection Applied to the Analysis of the International Variation in Diet
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Using Global Optimization to Explore Multiple Solutions of Clustering Problems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Robust Clustering by Aggregation and Intersection Methods
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Clustering of real-world datasets is a complex problem. Optimization models seeking to maximize a fitness function assume that the solution corresponding to the global optimum is the best clustering solution. Unfortunately, this is not always the case, mainly because of noise or intrinsic ambiguity in the data. In this work we present a set of tools implementing classical and novel techniques to approach clustering in a systematic way, with an application example to a complex biological dataset. The tools deal with the problem of generating multiple clustering solutions, performing cluster analysis on such clusterings (i.e. Meta Clustering) and reducing the final number of clusterings by the appropriate application of different Consensus techniques. A subsequent crossing of prior knowledge to the obtained clusters helps the user in better understanding its meaning and validates the solutions.