Efficient clustering approach using incremental and hierarchical clustering methods

M, Srinivas and C, Krishna Mohan (2010) Efficient clustering approach using incremental and hierarchical clustering methods. In: 6th IEEE World Congress on Computational Intelligence, 18-23, July 2010, Barcelona; Spain.

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There are many clustering methods available and each of them may give a different grouping of datasets. It is proven that hybrid clustering algorithms give efficient results over the other algorithms. In this paper, we propose an efficient hybrid clustering algorithm by combining the features of leader's method which is an incremental clustering method and complete linkage algorithm which is a hierarchical clustering procedure. It is most common to find the dissimilarity between two clusters as the distance between their centorids or the distance between two closest (or farthest) data points. However, these measures may not give efficient clustering results in all cases. So, we propose a new similarity measure, known as cohesion to find the intercluster distance. By using this measure of cohesion, a two level clustering algorithm is proposed, which runs in linear time to the size of input data set. We demonstrate the effectiveness of the clustering procedure by using the leader's algorithm and cohesion similarity measure. The proposed method works in two steps: In the first step, the features of incremental and hierarchical clustering methods are combined to partition the input data set into several smaller subclusters. In the second step, subclusters are merged continuously based on cohesion similarity measure. We demonstrate the effectiveness of this framework for the web mining applications. © 2010 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Clustering approach; Clustering methods; Clustering procedure; Clustering results; Complete linkage algorithms; Data points; Data sets; Hier-archical clustering; Hierarchical clustering methods; Hybrid clustering algorithm; Incremental clustering; Input datas; Intercluster distance; Linear time; Other algorithms; Similarity measure; Sub-clusters; Web Mining
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Users 3 not found.
Date Deposited: 13 Oct 2014 06:08
Last Modified: 01 Sep 2017 09:27
URI: http://raiith.iith.ac.in/id/eprint/191
Publisher URL: https://doi.org/10.1109/IJCNN.2010.5596666
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