A clustering method based on boosting
| Autor: | Aristidis Likas, Dimitrios S. Frossyniotis, Andreas Stafylopatis |
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| Jazyk: | angličtina |
| Rok vydání: | 2004 |
| Předmět: |
Clustering high-dimensional data
DBSCAN partitions schemes Boosting (machine learning) Fuzzy clustering Computer science Single-linkage clustering Correlation clustering Conceptual clustering ensemble clustering computer.software_genre unsupervised learning Biclustering Artificial Intelligence CURE data clustering algorithm Consensus clustering Cluster analysis k-medians clustering Brown clustering Training set business.industry em algorithm Pattern recognition Determining the number of clusters in a data set Data set ComputingMethodologies_PATTERNRECOGNITION Data stream clustering Signal Processing Canopy clustering algorithm Unsupervised learning FLAME clustering Affinity propagation Computer Vision and Pattern Recognition Data mining Artificial intelligence Hierarchical clustering of networks business computer Software |
| Popis: | It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. (C) 2004 Elsevier B.V. All rights reserved. Pattern Recognition Letters |
| Databáze: | OpenAIRE |
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