Information theoretic clustering
| Autor: | Jose C. Principe, Erhan Gokcay |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Fuzzy clustering
business.industry Applied Mathematics Correlation clustering Constrained clustering Pattern recognition computer.software_genre Determining the number of clusters in a data set Data stream clustering Computational Theory and Mathematics Artificial Intelligence CURE data clustering algorithm Canopy clustering algorithm Computer Vision and Pattern Recognition Artificial intelligence Data mining business Cluster analysis computer Software Mathematics |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 24:158-171 |
| ISSN: | 0162-8828 |
| DOI: | 10.1109/34.982897 |
| Popis: | Clustering is an important topic in pattern recognition. Since only the structure of the data dictates the grouping (unsupervised learning), information theory is an obvious criteria to establish the clustering rule. The paper describes a novel valley seeking clustering algorithm using an information theoretic measure to estimate the cost of partitioning the data set. The information theoretic criteria developed here evolved from a Renyi entropy estimator (A. Renyi, 1960) that was proposed recently and has been successfully applied to other machine learning applications (J.C. Principe et al., 2000). An improved version of the k-change algorithm is used in optimization because of the stepwise nature of the cost function and existence of local minima. Even when applied to nonlinearly separable data, the new algorithm performs well, and was able to find nonlinear boundaries between clusters. The algorithm is also applied to the segmentation of magnetic resonance imaging data (MRI) with very promising results. |
| Databáze: | OpenAIRE |
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