| URN |
etd-0628114-161349 |
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| Author |
Tsung-hsien Lin |
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| Author's Email Address |
No Public. |
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| Statistics |
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| Department |
Computer Science and Engineering |
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| Year |
2013 |
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| Semester |
2 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
An Elastic Net Algorithm for Automatic Clustering |
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| Date of Defense |
2014-06-26 |
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| Page Count |
52 |
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| Keyword |
automatic clustering
number of clusters
clustering
elastic net clustering algorithm
non-linearly separable data
|
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| 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 al-
gorithm is presented in this thesis to deal with exactly the two issues: partitioning non-linearly
separable data and automatically determining the number of clusters. To evaluate the perfor-
mance of the proposed algorithm, we compare it with several state-of-the-art methods using
several well-known datasets. The experimental results show that not only can the proposed
algorithm find the appropriate number of clusters, it can also provide a higher accuracy rate
than all the other methods compared in this study for most datasets. |
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| Advisory Committee |
Chu-sing Yang - chair
Tzung-Pei Hong - co-chair
Chun-Wei Tsai - co-chair
Ming-chao Chiang - advisor
|
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| Files |
Indicate in-campus at 99 year and off-campus access at 99 year. |
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| Date of Submission |
2014-08-20 |
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