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Principal curves, as a nonlinear generalization of principal components, are a common tool used in multivariate analysis for ends like dimensionality reduction and feature extraction. However, one of the difficulties that arise when utilizing this technique is that efficiency of existing principal curves algorithms is often low when dealing with large data set owing to high computational complexity...
Fuzzy Associative Classification has attracted remarkable research attention for knowledge discovery and business analytics in recent years due to its merits in accuracy and linguistic modeling. Furthermore, it is deemed meaningful to construct an associative classifier with a compact set of rules (i.e., compactness), which is easy to understand and use in decision making. This paper introduces a...
Fuzzy clustering has been one of the commonly used vehicles to construct information granules (whose description is provided in terms of prototypes and partition matrices). The quality of resulting information granules can be assessed by quantifying how well the original numeric data from which information granules have been constructed can be represented (granulated) by information granules and subsequently...
The clustering of web search has become a very interesting research area among academic and scientific communities involved in information retrieval. Clustering of web search result systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for web document clustering already...
The aim of this paper is designing a new approach for objective function- based fuzzy clustering. A new algorithm will be proposed for possibilistic c-means (PCM)-based models. This PCM-based algorithm uses fuzzy relations. In order to consider both separation between clusters and compactness within clusters, fuzzy relations will be applied. For verifying the efficiency of the proposed algorithm,...
The Fuzzy Joint Points (FJP) method which comprehends fuzziness in a level-based point of view is handled. At each iteration of the clustering process, unlike the classical fuzzy clustering in which the membership degrees of the points to the clusters are determined, the points which constitute the α-level sets are determined in FJP algorithm. In this study, some theoretical results are given for...
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