Cluster Tendency Methods for Visualizing the Data Partitions
M Suleman Basha1, S K Mouleeswaran2, K Rajendra Prasad3
1M Suleman Basha, Research Scholar, Dept. of CSE, Dayananda Sagar University, Bangalore, Karnataka, India.
2Dr. S K Mouleeswaran, Dept. of CSE, Dayananda Sagar University, Bangalore, Karnataka.
3Dr. K Rajendra Prasad, Dept. of CSE, Institute of Aeronautical Engineering, Hyderabad, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 02 September 2019. | Manuscript published on 30 September 2019. | PP: 2978-2982 | Volume-8 Issue-11, September 2019. | Retrieval Number: K22850981119/2019©BEIESP | DOI: 10.35940/ijitee.K2285.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering is widely used technique for grouping of data objects based on similarity features. The similarity features are derived from the similarity or dissimilarity metrics like Euclidean, cosine etc. Traditional clustering methods such as k-means, and other graph-based techniques are major techniques for discovery of clusters. However, these methods require user interference for determining the number of clusters initially. Determining the number of clusters for given data is known as cluster tendency. There is chance for getting poor clustering results when using either k-means or graph-based clustering methods with intractable value of ‘k’ by user. Thus, it is required to focus on cluster tendency methods for knowing prior knowledge about number of clusters in clustering. This paper presents the various visual access tendency (VAT) methods for good assessment of number of clusters.
Keywords: Clustering, Cluster Tendency, Similarity Measures, VAT
Scope of the Article: Cloud, Cluster, Grid and P2P Computing