Hybrid Clustering Approach in Data Mining

Authors

  • Vaishali  
  • Shagun  

Keywords:

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

Abstract

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.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-06-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vaishali, Shagun, " Hybrid Clustering Approach in Data Mining, IInternational 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.