Book ChapterDOI
Precision of Rough Set Clustering
Pawan Lingras,Min Chen,Duoqian Miao +2 more
- pp 369-378
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TLDR
This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.Abstract:
Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.read more
Citations
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A method for discovering clusters of e-commerce interest patterns using click-stream data
TL;DR: An improved leader clustering algorithm is constructed and three typical user interest patterns are derived based on a real click-stream dataset to provide significant assistances on webpage optimization and personalized recommendation.
Journal ArticleDOI
Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations
TL;DR: A framework based on preference or dominance relations that helps us qualitatively analyze a clustering scheme is described and is shown to be useful for combining clustering schemes that are based on different criteria.
Book ChapterDOI
Distributed Data Clustering: A Comparative Analysis
TL;DR: This paper compares the performance of two distributed clustering algorithms namely, Improved Distributed Combining Algorithm and Distributed K-Means algorithm against traditional Centralized Clustering Algorithm.
Journal ArticleDOI
Correlating Fuzzy and Rough Clustering
TL;DR: Experiments show that descriptive fuzzy clustering may not always produce results that are as accurate as direct application of rough clustering, and that combined approach to exploit inherent strengths of each technique is not always necessary.
Book ChapterDOI
Analysis of rough and fuzzy clustering
TL;DR: An experimental comparison of both the clustering techniques is provided and a procedure for conversion from fuzzy membership clustering to rough clustering is described, showing that descriptive fuzzy clustering may not always produce results that are as accurate as direct application ofrough clustering.
References
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Journal ArticleDOI
Constructive and algebraic methods of the theory of rough sets
TL;DR: This paper reviews and compares constructive and algebraic approaches in the study of rough set algebras and states axioms that must be satisfied by the operators.