Browsing by Author "Tseng, Frank S. C."

Now showing 1 - 6 of 6

An Integration of Fuzzy Association Rules and WordNet for Document Clustering
(2009)
Chen, Chun-Ling
;
Tseng, Frank S. C.
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
With the rapid growth of text documents, document clustering has become one of the train 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, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an effective Fuzzy Frequent Itemset-based Document Clustering (F(2)IDC) approach that combines fuzzy association rule mining with the background knowledge embedded in WordNet. A term hierarchy generated from WordNet is applied to discovery fuzzy frequent itemsets as candidate cluster labels for grouping documents. We have conducted experiments to evaluate our approach on Reuters-21578 dataset. The experimental result shows that our proposed method outperforms the accuracy quality of FIHC, HFTC, and UPGMA.
An integration of fuzzy association rules and WordNet for document clustering
(2011-09-01)
Chen, Chun-Ling
;
Tseng, Frank S. C.
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
With the rapid growth of text documents, document clustering technique is emerging for efficient document retrieval and better document browsing. Recently, some methods had been proposed to resolve the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels by using frequent itemsets derived from association rule mining for clustering documents. In order to improve the quality of document clustering results, we propose an effective Fuzzy Frequent Itemset-based Document Clustering (F(2)IDC) approach that combines fuzzy association rule mining with the background knowledge embedded in WordNet. A term hierarchy generated from WordNet is applied to discover generalized frequent itemsets as candidate cluster labels for grouping documents. We have conducted experiments to evaluate our approach on Classic4, Re0, R8, and WebKB datasets. Our experimental results show that our proposed approach indeed provide more accurate clustering results than prior influential clustering methods presented in recent literature.
An integration of Word Net and fuzzy association rule mining for multi-label document clustering
(2010-11-01)
Chen, Chun-Ling
;
Tseng, Frank S. C.
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
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 document set, and the initial representation of all documents is further enriched by using hypernyms of WordNet in order to exploit the semantic relations between terms. Then, a fuzzy association rule mining algorithm for texts is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, each document is dispatched into more than one target cluster by referring to these candidate clusters, and then the highly similar target clusters are merged. We conducted experiments to evaluate the performance based on Classic, Re0, R8, and WebKB datasets. The experimental results proved that our approach outperforms the influential document clustering methods with higher accuracy. Therefore, our approach not only provides more general and meaningful labels for documents, but also effectively generates overlapping clusters. (C) 2010 Elsevier B.V. All rights reserved.
An integration of Word Net and fuzzy association rule mining for multi-label document clustering
(2010-11-01)
Chen, Chun-Ling
;
Tseng, Frank S. C.
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
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 document set, and the initial representation of all documents is further enriched by using hypernyms of WordNet in order to exploit the semantic relations between terms. Then, a fuzzy association rule mining algorithm for texts is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, each document is dispatched into more than one target cluster by referring to these candidate clusters, and then the highly similar target clusters are merged. We conducted experiments to evaluate the performance based on Classic, Re0, R8, and WebKB datasets. The experimental results proved that our approach outperforms the influential document clustering methods with higher accuracy. Therefore, our approach not only provides more general and meaningful labels for documents, but also effectively generates overlapping clusters. (C) 2010 Elsevier B.V. All rights reserved.
Extending the UML concepts to transform natural language queries with fuzzy semantics into SQL
(2006-09-01)
Tseng, Frank S. C.
;
Chen, Chun-Ling
;
資訊工程學系
;
Department of Computer Science
Database applications tend toward getting more versatile and broader to comply with the expansion of various organizations. However, naive users usually suffer from accessing data arbitrarily by using formal query languages. Therefore, we believe that accessing databases using natural language constructs will become a popular interface in the future. The concept of object-oriented modeling makes the real world to be well represented or expressed in some kinds of logical form. Since the class diagram in UML is used to model the static relationships of databases, in this paper, we intend to study how to extend the UML class diagram representations to capture natural language queries with fuzzy semantics. By referring to the conceptual schema throughout the class diagram representation, we propose a methodology to map natural language constructs into the corresponding class diagram and employ Structured Object Model (SOM) methodology to transform the natural language queries into SQL statements for query executions. Moreover, our approach can handle queries containing vague terms specified in fuzzy modifiers, like 'good' or 'bad'. By our approach, users obtain not only the query answers but also the corresponding degree of vagueness, which can be regarded as the same way we are thinking. (c) 2006 Elsevier B.V. All rights reserved.
Mining fuzzy frequent itemsets for hierarchical document clustering
(2010-03-01)
Chen, Chun-Ling
;
Tseng, Frank S. C.
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
As text documents are explosively increasing in the Internet, the process of hierarchical document clustering has been proven to be useful for grouping similar documents for versatile applications. However, most document clustering methods still suffer from challenges in dealing with the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels. In this paper, we will present an effective Fuzzy Frequent Item-set-Based Hierarchical Clustering (F(2)IHC) approach, which uses fuzzy association rule mining algorithm to improve the clustering accuracy of Frequent Item-set-Based Hierarchical Clustering (FIHC) method, In our approach, the key terms will be extracted from the document set, and each document is pre-processed into the designated representation for the following mining process. Then, a fuzzy association rule mining algorithm for text is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, these documents will be clustered into a hierarchical cluster tree by referring to these candidate clusters. We have conducted experiments to evaluate the performance based on Classic4, Hitech, ReO, Reuters, and Wap datasets. The experimental results show that our approach not only absolutely retains the merits of FIHC, but also improves the accuracy quality of FIHC. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.