Performance evaluation of hierarchical clustering algorithms
E. Gothai, P. Balasubramanie
Abstract
E. Gothai, P. Balasubramanie
Abstract
Clustering, an supervised learning process is a challenging problem. Clustering result quality improves the overall structure. In this article, we propose an incremental stream of hierarchical clustering and improve the efficiency, reduce time consumption and accuracy of text categorization algorithm by forming an exact sub clustering. In this paper we propose a new method called multilevel clustering which a combination is of supervised and an unsupervised technique for form the clustering. In this method we form four levels of clustering. The proposed work uses the existing clustering algorithm. We develop and discuss algorithms for multilevel clustering method to achieve the best clustering experiment.
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Clustering, an supervised learning process is a challenging problem. Clustering result quality improves the overall structure. In this article, we propose an incremental stream of hierarchical clustering and improve the efficiency, reduce time consumption and accuracy of text categorization algorithm by forming an exact sub clustering. In this paper we propose a new method called multilevel clustering which a combination is of supervised and an unsupervised technique for form the clustering. In this method we form four levels of clustering. The proposed work uses the existing clustering algorithm. We develop and discuss algorithms for multilevel clustering method to achieve the best clustering experiment.
Key concepts: Cluster analysis, CURE data clustering algorithm, Correlation clustering, Canopy clustering algorithm, Data stream clustering, Single-linkage clustering, Computer science, Fuzzy clustering