Automatic elastic net clustering algorithm.
Journal
2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014, San Diego, CA, USA, October 5-8, 2014
Pages
2768-2773
Date Issued
2014
Author(s)
Abstract
Clustering has always been playing a vital role in many different disciplines because it is an important tool for analyzing a set of unknown input patterns. However, some important issues related to clustering, such as automatically determining the number of clusters and partitioning non-linearly separable data, are never fully solved even though many researchers work on this subject for a long time. As such, a novel method based on the so called elastic net clustering algorithm is presented in this paper to deal with exactly the two issues: partitioning non-linearly separable data and automatically determining the number of clusters. To evaluate the performance of the proposed algorithm, several well-known datasets are used. The experimental results show that not only can the proposed algorithm find the appropriate number of clusters, but it can also provide a higher accuracy rate than all the other methods compared in this study for most datasets.
Type
conference paper