Improving the Efficiency of Image Clustering using Modified Non Euclidean Distance Measures in Data Mining


  • santhi P Paavai Engineering College
  • V.Murali Bhaskaran Paavai College of Engineering Pachal, Namakkal, India


Data Mining, Image Mining, Kmeans, Fuzzy Kmeans, Euclidean Distance


The Image is very important for the real world to transfer the messages from any source to destination. So, these images are converted in to useful information using data mining techniques. In existing all the research papers using kmeans and fuzzy k means with euclidean distance for image clustering. Here, each cluster needs its own centric for cluster calculation and the euclidean distance calculate the distance between the points. In clustering this process of distance calculation did not give efficient result. For make this in to efficient, this research paper proposes the non Euclidean distance measures for distance calculation. Here, the logical points are used to find the cluster. The result shows that image clustering based on the modified non Euclidean distance and the performance shows the efficiency of non euclidean distance


Nor Ashidi Mat Isa; Samy Salamah, A, Umi Kalthum Ngah; Adaptive Fuzzy Moving Kmeans Clustering Algorithm for Image Segmentation, IEEE Trans on Knowledge and Data Engineering, 2145-2153, 2009.

Siti Noraini Sulaiman; Nor Ashidi Mat Isa; Adaptive Fuzzy-K-means Clustering Algorithm for Image Segmentation, IEEE Trans on Knowledge and Data Engineering, 2661-2668, 2010.

Maduri, A.Tayal;Raghuwanesh,M.M.; Review on various Clustering Methods for Image Data, J. of Emerging Trends in Computing and Information Sciences, 34-38, 2010.

Keh-Shih Chuang; Hong-Long Tzeng; Sharon Chen; Jay wu; Tzong-Jer Chen; Fuzzy c-Means Clustering with spatial information for image segmentation,Elseiver, 9-15, 2006.

Sneha Silvia, A.; Vamsidhar, Y.; Sudhakar,G.; Color Image Clustering using K-Means,IJCST, 11-13, 2011.

Vasuda, P.; Satheesh, S.; Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation, IJCSE, 1713-1715, 2010.

Fahim, A.M.; Saake, G.; Salem, A.M.; Torkey, F.A.; K-Means for Spherical Clusters with Large Variance in Sizes, World Academy of Science, Engineering and Technology, 177-182, 2008.

John Peter, S.; Minimum Spanning Tree-based Structural Similarity Clustering for Image Mining with Local Region Outliers,Int. J. of Computer Applications, 33-40, 2010.

Chawan, P.M.; Saurabh Bhonde,R.; Shirish Patil; Concentric Circle-Based Image Signature for Near-Duplicate Detection in Large Databases,Electronics and telecommunication Research Institute, 2010.



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.