DocumentCode
541782
Title
Performance evaluation of hierarchical clustering algorithms
Author
Gothai, E. ; Balasubramanie, P.
Author_Institution
Dept. of CT-PG, Kongu Eng. Coll., Perudurai, India
fYear
2010
fDate
27-29 Dec. 2010
Firstpage
457
Lastpage
460
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.
Keywords
data mining; learning (artificial intelligence); pattern clustering; performance evaluation; hierarchical clustering algorithms; incremental stream; multilevel clustering; performance evaluation; supervised learning; text categorization; Accuracy; Algorithm design and analysis; Artificial neural networks; Banking; Clustering algorithms; Databases; Logic gates; cluster formation; data mining; edit distance learning; learning; similarity measure; supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Communication and Computational Intelligence (INCOCCI), 2010 International Conference on
Conference_Location
Erode
Type
conf
Filename
5738773
Link To Document