A New Version of the Price‘s Algorithm for Global Optimization
Journal of Global Optimization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Genetic approach helps to speed classical Price algorithm for global optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
Global optimization in clustering using hyperbolic cross points
Pattern Recognition
Clustering and visualization approaches for human cell cycle gene expression data analysis
International Journal of Approximate Reasoning
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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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In this work we propose a scientific data exploration methodology and software environment that permits to obtain both data meta-clustering and interactive visualizations. Our approach is based on an elaboration pipeline, including data reading, multiple clustering solution generation, meta clustering and consensus clustering. Each stage is supported by dedicated visualization and interaction tools. Involved techniques include a Price based global optimization algorithm able to build a set of solutions that are local minima of the K-means objective function; different consensus methods aimed to reduce the set of solutions; tools for the interactive hierarchical agglomeration of clusterings and for the exploration and visualization of the space of clustering solutions.