Optimal Value for Number of Clusters in a Dataset for Clustering Algorithm
Jayashree1, Shivaprakash T2
1Jayashree*, Department of Computer Science and Engineering, Vijaya Vittala Institute of Technology, Bangalore (Karnataka), India.
2Dr. Shivaprakash T, Professor, Department of Computer Science and Engineering, Vijaya Vittala Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on February 15, 2022. | Revised Manuscript received on February 23, 2022. | Manuscript published on April 30, 2022. | PP: 24-29 | Volume-11 Issue-4, April 2022. | Retrieval Number: 100.1/ijeat.D34170411422 | DOI: 10.35940/ijeat.D3417.0411422
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Abstract: It is essential to know the parameters required to clustering the dataset. One of the parameters is the number of clusters k and it is very important to select the k value to get deficient results on clustering. There are few algorithms to find the k value for k-means algorithm and it requires specifying a maximum value for k or a range of values for k as an input. This paper proposes a novel method Optimal cluster number estimation algorithm (OCNE) to find the optimal number of clusters without specifying the maximum or range of k values or knee point detection in the graph. In the experiment, this method is compared with the different existing methods with deficient real-world as well as synthetic datasets and provides good performance.
Keywords: Cluster, Convex Cluster, Data Mining, Feature Extraction, K-Means
Scope of the Article: Data Mining