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The incremental method for fast computing the rough fuzzy approximations?
Publication year: 2010
Source: Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 7 September 2010
Yi, Cheng , Duoqian, Miao
The lower and upper approximations are basic concepts in rough fuzzy set theory. The effective computation of approximations is very important for improving the performance of related algorithms. This paper proposed and proved two incremental methods for fast computing the rough fuzzy approximations, one starts from the boundary set, the other is based on the cut sets of a fuzzy set. Then some illustrative examples are conducted. Consequently, two algorithms corresponding to the two incremental methods are put forward respectively. In order to test the efficiency of algorithms?some experiments are made on a large soybean data set from UCI?The experimental...
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Editorial Board
Publication year: 2010
Source: Data & Knowledge Engineering, Volume 69, Issue 10, October 2010, Page IFC
[No author name available]
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An integration of WordNet and fuzzy association rule mining for multi-label document clustering
Publication year: 2010
Source: Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 31 August 2010
Chun-Ling, Chen , Frank S.C., Tseng , Tyne, Liang
With the rapid growth of text documents, document clustering has become one of the main techniques for organizing large amount of documents into a small number of meaningful clusters. However, there still exist several challenges for document clustering, such as high dimensionality, scalability, accuracy, meaningful cluster labels, overlapping clusters, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an effective Fuzzy-based Multi-label Document Clustering (FMDC) approach that integrates fuzzy association rule mining with an existing ontology WordNet to alleviate these problems. In our approach, the key terms will be extracted from the...
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Scaling up Top-K Cosine Similarity Search
Publication year: 2010
Source: Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 31 August 2010
Shiwei, Zhu , Junjie, Wu , Hui, Xiong , Guoping, Xia
Recent years have witnessed an increased interest in computing cosine similarity in many application domains. Most previous studies require the specification of a minimum similarity threshold to perform the cosine similarity computation. However, it is usually difficult for users to provide an appropriate threshold in practice. Instead, in this paper, we propose to search top-K strongly correlated pairs of objects as measured by the cosine similarity. Specifically, we first identify the monotone property of an upper bound of the cosine measure and exploit a diagonal traversalstrategy for developing a TOP-DATA algorithm. In addition, we observe that a diagonal traversal strategy...
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Indexing and querying XML using extended Dewey labeling scheme
Publication year: 2010
Source: Data & Knowledge Engineering, In Press, Accepted Manuscript, Available online 18 August 2010
Jiaheng, Lu , Xiaofeng, Meng , Tok Wang, Ling
Finding all the occurrences of a tree pattern in an XML database is a core operation for efficient evaluation of XML queries. The Dewey labeling scheme is commonly used to label an XML document to facilitate XML query processing by recording information on the path of an element. In order to improve the efficiency of XML tree pattern matching, we introduce a novel labeling scheme, called extended Dewey, which effectively extends the existing Dewey labeling scheme to combine the types and identifiers of elements in a label, and to avoid the scan of labels for internal query nodes to accelerate...
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