An improved documents classification technique using association rules mining

Qureshi, Mohammad Naved; Aldheleai, Hassan Faisal; Tamandani, Yahya Kord

doi:10.1109/icrcicn.2015.7434283

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…There are several classification approaches that address the problem in different ways, such as decision trees [Samsani 2016], Bayesian algorithms [Claster et al 2010], nearest neighbors [Qureshi et al 2015], and neural networks [Nazzal et al 2008].…”

Section: Predictive Analysismentioning

confidence: 99%

A Data Mining Study of Sindec Complaints in the Period 2013-2017

Sargiani¹,

Castro²,

Silva³

2020

Proceedings of the International Conferences on Internet Technologies &Amp; Society (ITS 2020) and Sustainability, Technology A

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The SINDEC's complaints database is an important source of information about the sectors and problems that cause complaints in Brazil. By analyzing these data, one can understand the main complaints and whether companies and institutions are working to solve such problems. The objective of this work is to apply data mining techniques, more specifically descriptive analyses and a classification algorithm, to extract knowledge from the data over the period 2013 and 2017.

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Section: Predictive Analysismentioning

confidence: 99%

A Data Mining Study of Sindec Complaints in the Period 2013-2017

Sargiani¹,

Castro²,

Silva³

2020

Proceedings of the International Conferences on Internet Technologies &Amp; Society (ITS 2020) and Sustainability, Technology A

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“…In Qureshi et al (2015), the authors proposed an associative classifier by combining the association rule data mining algorithm -Aprioriwith pruning methods to classify text documents. The results showed improvement in the accuracy of classification compared to other classical methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Indexing Arabic texts using association rule data mining

Haraty

Nasrallah

2019

LHT

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Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together. Findings The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works. Research limitations/implications The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results. Originality/value In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.

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