An Improved Graph based Rules Mining Technique from Text

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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. To improve the accuracy of mining association rules from textual database, we represent the documents as graphs to allow for a much more expressive document representation than the traditional text representation approaches. Document sets are represented as graph sets to which the proposed graph mining algorithm is applied to extract frequent subgraphs, which are then further processed to produce feature vectors (one per document). 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. The results determine that the proposed approach is better than existing text mining algorithms on almost all textual datasets.

Engineering World, E-ISSN: 2692-5079, Volume 2, 2020, Art. #12

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Wael Ahmad Alzoubi, "An Improved Graph based Rules Mining Technique from Text," Engineering World, vol. 2, pp. 76-81, 2020, DOI:
Wael Ahmad Alzoubi. An Improved Graph based Rules Mining Technique from Text. Engineering World. 2020;2:76-81.
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