An Effective Method of Image Mining using K-Medoid Clustering Technique

Authors

  • Jayaswal R Dept. of CSE/IT, Madhav Institute of Technology and Science, Gwalior, India
  • Jha J Dept. of CSE/IT, Madhav Institute of Technology and Science, Gwalior, India
  • Devesh R Dept. of CSE/IT, Madhav Institute of Technology and Science, Gwalior, India

Keywords:

Image Mining, RGB histogram descriptor, Edge Histogram Descriptor (EHD), Content Based Image Retrieval (CBIR), Clustering, K-Medoid Clustering Algorithm, Data Mining, Manhattan Similarity Measure

Abstract

The whole world is filled with a huge collection of digital data, digital images, and videos or can be anything that can be stored in a digitized manner. This data doesn't mean essentially anything. It is stored in an unorganized manner without any interpretation. Image Mining is an energetic concept for researchers. When there is a need to extract necessary information from the massive collection of image database through image mining techniques then this concept came into the picture. In this research paper, the proposed work is done through two steps. One is feature extraction, extract the features of images by RGBHist as a color feature and Edge Histogram Descriptor as a shape feature has taken to create feature dataset. While in second step K-Medoid clustering algorithm is applied to make good clusters and retrieval process is done from the clusters to increase the accuracy of the system. Manhattan similarity method is used a matching purpose from the query image. Three Database is used in this paper for testing the proposed image mining system.

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Published

2025-11-11

How to Cite

[1]
R. Jayaswal, J. Jha, and R. Devesh, “An Effective Method of Image Mining using K-Medoid Clustering Technique”, Int. J. Comp. Sci. Eng., vol. 5, no. 6, pp. 206–214, Nov. 2025.

Issue

Vol. 5 No. 6 (2017): IJCSE June Edition

Section

Research Article

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