Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Multilevel Based Hierarchical Clustering


Affiliations
1 Department of CSE, Kongu Engineering College, Perundurai, India
     

   Subscribe/Renew Journal


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

Algorithms, Clustering, Experimentation of Levenshtein Distance Method,Supervised Learning, Unsupervised Learning, Cluster Formation, Similarity Measure, Learning, Edit Distance Learning, Data Mining.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 272

PDF Views: 1




  • Multilevel Based Hierarchical Clustering

Abstract Views: 272  |  PDF Views: 1

Authors

E. Gothai
Department of CSE, Kongu Engineering College, Perundurai, India
P. Balasubramie
Department of CSE, Kongu Engineering College, Perundurai, India

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


Algorithms, Clustering, Experimentation of Levenshtein Distance Method,Supervised Learning, Unsupervised Learning, Cluster Formation, Similarity Measure, Learning, Edit Distance Learning, Data Mining.