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Content Provider | IEEE Xplore Digital Library |
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Author | Chun-Wei Tsai Tsung-Hsien Lin Ming-Chao Chiang |
Copyright Year | 2014 |
Description | Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Ilan Univ., Yilan, Taiwan (Chun-Wei Tsai) || Dept. of Comput. Sci. & Eng., Nat. Sun Yat-sen Univ., Kaohsiung, Taiwan (Tsung-Hsien Lin; Ming-Chao Chiang) |
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. |
Starting Page | 2768 |
Ending Page | 2773 |
File Size | 172979 |
Page Count | 6 |
File Format | |
ISBN | 9781479938407 |
DOI | 10.1109/SMC.2014.6974347 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-10-05 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Clustering algorithms Classification algorithms Accuracy Indexes Algorithm design and analysis Traveling salesman problems Approximation algorithms cluster validity index Elastic net algorithm clustering algorithm |
Content Type | Text |
Resource Type | Article |
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