Clustering Algorithm of Similarity Segmentation based on Point Sorting
- 10.2991/lemcs-15.2015.91How to use a DOI?
- Similarity; Point sorting; Segmentation; Wavelet filter
Researchers propose a clustering algorithm of similarity segmentation based on point sorting to improve the clustering performance. Taking full advantage of segmentation sorting of the clustering algorithm based on minimum spanning tree, the algorithm uses a variety of methods for different situations to sort these cluster elements with their similarity and segment them where there are large changes in their similarity to obtain cluster results. In order to compare with the performance of the method, researchers select some traditional cluster analysis methods like k-means, hierarchical clustering and density clustering with noise data, etc. In the experimental testing, researchers select three sets of two-dimensional artificial data sets and four sets of real data sets as test data. Besides, three evaluation indexes are applied to measure the quality of clustering. The simulation results in test data show that this algorithm can improve the accuracy of the algorithm effectively and achieved good clustering performance.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hanbing Li AU - Yan Wang AU - Lan Huang AU - Mingda Li AU - Ying Sun AU - Hanyuan Zhang PY - 2015/07 DA - 2015/07 TI - Clustering Algorithm of Similarity Segmentation based on Point Sorting BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 475 EP - 482 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.91 DO - 10.2991/lemcs-15.2015.91 ID - Li2015/07 ER -