Assessment of Cluster Tendency Methods for Visualizing the Data Partitions | Chapter 4 | Advanced Aspects of Engineering Research Vol. 11

 Clustering is a frequently used technique for grouping data objects based on similarity features. To generate similarity features, similarity metrics such as Euclidean, cosine, and others are employed. To locate clusters, traditional clustering methods such as k-means and other graph-based algorithms are often utilised. These methods, on the other hand, require user participation in order to determine the number of clusters. Traditional clustering algorithms partition data without first taking into account the number of clusters or cluster tendency. There is a risk of poor clustering performance when employing k-means or graph-based clustering methods with an intractable "k" value provided by the consumer. As a result, it is critical to focus on cluster tendency approaches for prior knowledge of the number of clusters in clustering. This paper discusses the numerous visual access tendency (VAT) approaches for calculating the number of clusters.

Author (s) Details

M. Suleman Basha
Department of CSE, Dayananda Sagar University, Bangalore, India.

S. K. Mouleeswaran
Department of CSE, Dayananda Sagar University, Bangalore, India.

K. Rajendra Prasad
Department. of CSE, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, India.

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