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Comparison of centroid-based clustering algorithms in the context of divide and conquer paradigm based FMST framework
Sabhijiit S. Sandhu, Ashwin R. Jadhav,
Published in IEEE
Volume: 2017-December
Pages: 219 - 224
The practice of using divide and conquer techniques to solve complex, time-consuming problems has been in use for a very long time. Here we evaluate the performance of centroid-based clustering techniques, specifically k-means and its two approximation algorithms, the k-means++ and k-means (also known as Scalable k-means++), as divide and conquer paradigms applied for the creation of minimum spanning trees. The algorithms will be run on different datasets to get a good evaluation of their respective performances. This is a continuation of our previous work carried out in developing the KMST+ algorithm in the context of fast minimum spanning tree (FMST) frameworks. © 2017 IEEE.
About the journal
JournalData powered by Typeset2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)
PublisherData powered by TypesetIEEE
Open Access0