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

Abstract

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 distancemeasures.

References

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Published
2014-01-03
How to Cite
P, santhi; BHASKARAN, V.Murali. Improving the Efficiency of Image Clustering using Modified Non Euclidean Distance Measures in Data Mining. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 9, n. 1, p. 56-61, jan. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/50>. Date accessed: 03 july 2020. doi: https://doi.org/10.15837/ijccc.2014.1.50.

Keywords

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