Text clustering approach based on maximal frequent term sets

Su, Chong; Chen, Qingcai; Wang, Xiaolong; Meng, Xianglong

doi:10.1109/icsmc.2009.5346313

Cited by 8 publications

(11 citation statements)

References 8 publications

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“…To attain this requirement, combining the ontology based text mining method (OTMM) [12] and frequent itemsets [20][21][22][23], an ontology based frequent itemset method (OFIM) is proposed for research proposal grouping, and the framework of the proposed method is shown in Figure 2. As can be seen from Figure 2, a research ontology is first built by the keywords collected from the research projects in latest five years, and research proposals are classified into each discipline.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

“…Frequent itemsets, which can make use of relationship among documents, can improve the quality of clustering [20]. So, frequent itemset methods have been introduced for text clustering problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, specific phases to cluster proposals mainly focus on the diversity of applicants' characteristics [10][11], while ignoring the content similarities of research proposals. Through text mining has been introduced for research proposal grouping [12], due to the high computing complexity and the difficulty of controlling clustering results, this method may lead to poor performance when the number of research proposals is large [20]. To cover this research gap, this paper contributes an ontology based frequent itemset method (OFIM) to support research proposal grouping.…”

Section: Introductionmentioning

confidence: 99%

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An Ontology Based Frequent Itemset Method to Support Research Proposal Grouping for Research Project Selection

2013

2013 46th Hawaii International Conference on System Sciences

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Research proposal grouping is one of the most important tasks for research project selection in research funding agencies. In this paper, a novel ontology based frequent itemset method is proposed to deal with research proposal grouping problem. In the proposed method, a research ontology is firstly constructed to standardize research keywords. Secondly, frequent itemsets with different support degrees are extracted from research proposals based on research ontology. Thirdly, a new measure of similarity degree between two research proposals is developed and then a clustering algorithm is proposed to classify research proposals based on the similarity degree, in which some parameters are discussed, and the proper parameters are selected. Finally, when the number of research proposals in some clusters is still large, research proposals are further divided into small groups, in which the number of research proposals is approximately equal. The proposed method is validated based on the selection process at the National Natural Science Foundation of China (NSFC). The experimental results show that our proposed method can improve the efficiency and effectiveness of research proposal grouping, and is a potential and alternative one to support research project selection processes in other governments and private research funding agencies.

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Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Ontology Based Frequent Itemset Method to Support Research Proposal Grouping for Research Project Selection

2013

2013 46th Hawaii International Conference on System Sciences

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“…Malik and Kender [5] propose frequent closed interestingness itemset (FCII) by utilizing some interestingness measurements to construct the termsets. Su et al [6] propose a text clustering based on maximal frequent itemset (MFI).…”

Section: Frequent Termset Based Clusteringmentioning

confidence: 99%

“…The advantages of this approach compared to distance based clustering such as K-means, agglomerative and divisive approaches have been reported in some papers [2,3,4,5,6] i.e.…”

Section: Introductionmentioning

confidence: 99%

Text Clustering using Frequent Contextual Termset

Akhriza

2011

2011 International Conference on Information Management, Innovation Management and Industrial Engineering

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We introduce frequent contextual termset (FCT) as an alternative concept of termset construction for text clustering which is produced from the interestingness of documents. Comparing to state-of-art termset, the proposed approach has some advantages: (1) more efficient in termset production (2) more effective in storing the vocabulary amongst documents which express the context amongst documents and (3) more suitable to discover specificity of dataset. To utilize FCT we also introduce frequent contextual termset based hierarchical clustering (FCTHC) which adopts the concept of centroids in K-means with some main differences. The experiment shows that FCT is the correct pattern to perform text clustering and FCTHC provides flexible approach in clusters construction.

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Frequent Term-Based Text Clustering Using Hidden Support

Patil

Thakur

2018

Lecture Notes in Networks and Systems

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Text clustering approach based on maximal frequent term sets

Cited by 8 publications

References 8 publications

An Ontology Based Frequent Itemset Method to Support Research Proposal Grouping for Research Project Selection

An Ontology Based Frequent Itemset Method to Support Research Proposal Grouping for Research Project Selection

Text Clustering using Frequent Contextual Termset

Frequent Term-Based Text Clustering Using Hidden Support

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