WSEAS Transactions on Systems and Control
Print ISSN: 1991-8763, E-ISSN: 2224-2856
Volume 15, 2020
Dynamic Graph based Method for Mining Text Data
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Abstract: An improved graph based association rules mining (ARM) approach to extract association rules from text databases is proposed in this paper. The text document in the proposed technique is read only once to look for the terms whose occurrences are greater than some threshold value, these terms are stored in a file with their frequencies, then they are represented as nodes in a weighted directed graph where edges represent relations between these terms, the edges will denote the associations between terms while the edges’ weights denote the strength or confidence of these rules. The proposed method is called Dynamic Graph based Rule Mining from Text (DGRMT) because the graph is built level by level according the length of a sentence (number of frequent terms). Weighted subgraph mining is used to ensure the efficiency and throughput of the proposed technique; only the most frequent subgraphs are extracted. The proposed technique is validated and evaluated using real world textual data sets and compared with one of the best graph based rule mining technique, which is algorithm for Generating Association Rules based on Weighting scheme (GARW). The results determine that the proposed approach is better than GARW on almost all textual datasets.
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Pages: 453-458
DOI: 10.37394/23203.2020.15.45