Review on Deciding Optimal Number of Clusters in k-means Clustering

Registration ID: IJNRD_194029

Published ID: IJNRD2305143

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Kushal Jain , Mohit Kumar

Number of clusters, k means, elbow method, silhouette, gap statistics, non parametric, curvature based method

In machine learning, the clustering approach is used to classify related data points or objects of a sizable dataset and is frequently applied in various fields, including image analysis, bioinformatics, and research fields like economics and biology. It is crucial to understand the initial parameters before clustering since patterns from any clustering algorithm depend on those parameters. Although there are various clustering techniques, we will concentrate on k-means grouping in this study which serves as the quickest and easiest. We focus on approaches for figuring out how many clusters are needed as clustering inputs. We can request in advance from end users that they submit the number of groups of the data point, but this is not practical because the end user needs to have knowledge regarding each dataset. There are numerous tackles that may be utilized for this, but we only concentrate on a few recent proposed and some widely used techniques.

Published Paper   E-Certificate

Kushal Jain & Mohit Kumar (May-2023). Review on Deciding Optimal Number of Clusters in k-means Clustering. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), b307-b313. https://ijnrd.org/papers/IJNRD2305143.pdf

Volume 8 Issue 5, May-2023

Pages : b307-b313

Paper Reg. ID: IJNRD_194029

Published Paper Id: IJNRD2305143

Downloads: 000121989

Research Area: Computer Engineering 

Country: Hansi, Haryana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305143.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305143

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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