Knowledge Discovery in Text Mining Technique Using Association Rules Extraction

Purpose The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics. Design/methodology/approach This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1). Findings The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

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Discovering Personalized Novel Knowledge from Text

Handbook of Research on Text and Web Mining Technologies ◽

10.4018/978-1-59904-990-8.ch018 ◽

2010 ◽

pp. 301-313

Author(s):

Yi-fang Brook Wu ◽

Xin Chen

Keyword(s):

Text Mining ◽

Knowledge Discovery ◽

Association Rules ◽

Association Rule ◽

Noun Phrases ◽

Background Knowledge ◽

Semantic Distance ◽

Concept Hierarchy ◽

Better Than

This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules derived and many already known to the user. Our proposed algorithm derives user’s background knowledge from a set of documents provided by the user, and exploits such knowledge in the process of knowledge discovery from text. Keywords are extracted from background documents and clustered into a concept hierarchy that captures the semantic usage of keywords and their relationships in the background documents. Target documents are retrieved by selecting documents that are relevant to the user’s background. Association rules are discovered among noun phrases extracted from target documents. Novelty of an association rule is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment shows that our novelty measure performs better than support and confidence in identifying novel knowledge.

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Information Retrieval (IR) and Extracting Associative Rules

Business Intelligence ◽

10.4018/978-1-4666-9562-7.ch037 ◽

2016 ◽

pp. 713-732

Author(s):

Asmae Dami ◽

Mohamed Fakir ◽

Belaid Bouikhalene

Keyword(s):

Information Retrieval ◽

Text Mining ◽

Knowledge Discovery ◽

Association Rules ◽

Retrieval System ◽

Information Retrieval System ◽

User Needs ◽

Retrieval Process ◽

Research Themes ◽

Document Database

This chapter is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

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Information Retrieval (IR) and Extracting Associative Rules

Journal of Information Technology Research ◽

10.4018/jitr.2014100104 ◽

2014 ◽

Vol 7 (4) ◽

pp. 42-62

Author(s):

Asmae Dami ◽

Mohamed Fakir ◽

Belaid Bouikhalene

Keyword(s):

Information Retrieval ◽

Text Mining ◽

Knowledge Discovery ◽

Association Rules ◽

Retrieval System ◽

Information Retrieval System ◽

User Needs ◽

Retrieval Process ◽

Research Themes ◽

Document Database

This paper is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

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A new condition assessment method for distribution transformers based on operation data and record text mining technique

2016 China International Conference on Electricity Distribution (CICED) ◽

10.1109/ciced.2016.7576179 ◽

2016 ◽

Cited By ~ 4

Author(s):

Cheng Xie ◽

Guoping Zou ◽

Huifang Wang ◽

Yongtao Jin

Keyword(s):

Text Mining ◽

Condition Assessment ◽

Assessment Method ◽

Mining Technique ◽

Distribution Transformers

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Exploring Insights through Visualization of Association Rules from Text Mining Statistics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.310.567 ◽

2013 ◽

Vol 310 ◽

pp. 567-571

Author(s):

Arun Thotapalli Sundararaman

Keyword(s):

Text Mining ◽

Association Rules ◽

Decision Makers ◽

Analysis Of Knowledge ◽

Strength Of Association ◽

The Way

Visualization is an important technique for analysis of knowledge derived from text mining. While different approaches exist for visualization, this paper presents a novel way of visualizing the strength of association between multiple terms that summarizes association in the form of a matrix. This approach is expected to improve the way decision makers analyze insights from text mining.

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