[1]CHENG Yang,WANG Shitong.A multiple alternative clusterings mining algorithm using locality preserving projections[J].CAAI Transactions on Intelligent Systems,2016,11(5):600-607.[doi:10.11992/tis.201508022]
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A multiple alternative clusterings mining algorithm using locality preserving projections

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 600-607 Column: 学术论文—机器学习 Public date: 2016-11-01
Title:
A multiple alternative clusterings mining algorithm using locality preserving projections
Author(s):
CHENG Yang;  WANG Shitong
School of Digit Media, Jiangnan University, Wuxi 214122, China
Keywords:
alternative clustering; unsupervised learning; manifold learning; multiple clusterings; eigendecomposition
CLC:
TP18
DOI:
10.11992/tis.201508022
Abstract:
Most clustering algorithms typically find just one single result for the data inputted. Considering that the complexity of the data is generally high, combined with the need to allow the data to be viewed from different perspectives (on the basis of ensuring reasonableness), means that clustering results are often not unique. We present a new algorithm RLPP for an alternative clustering generation method. The objective of RLPP is to find a balance between clustering quality and dissimilarity using a subspace manifold learning technique in a new subspace so that a variety of clustering results can be generated. Experimental results using both linear and nonlinear datasets show that RLPP successfully provides a variety of alternative clustering results, and is able to outperform or at least match a range of existing methods.
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