Hybrid Clustering Approach in Data Mining

Authors(2) :-Vaishali, Shagun

Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. However, its performance in terms of global optimality depends heavily on both the selection of k and the selection of the initial cluster centres. Mean Shift clustering algorithm does not rely upon a priori knowledge of the number of clusters. Therefore, mean-shift can be utilized in initial phase for finding number of clusters and k-means in second phase for proper segmentation. In this paper, the importance of two-phase approach has been studied for images with non-uniform and noisy background like ultrasound images and Infrared images.

Authors and Affiliations

Vaishali

Shagun

Image Segmentation, Clustering, K-Means, Mean Shift.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 695-699
Manuscript Number : CSEIT1835176
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Vaishali, Shagun, "Hybrid Clustering Approach in Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.695-699, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1835176

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