A survey of association rule mining in text applications

Manimaran, J.; Velmurugan, T.

doi:10.1109/iccic.2013.6724258

Cited by 11 publications

(18 citation statements)

References 8 publications

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“…Applying DM techniques to extract interesting and useful knowledge is challenging because of the information overloading, higher duplication rates, and ambiguities of data [5]. DM in the text is known as Text Mining (TM) and is generally used to extract meaningful patterns in a collection of text data using Natural Language Processing (NLP) techniques such as Information Extraction (IE) to transform unstructured data into a structured format [6,7]. One of the main operations in TM is frequent itemset mining for identifying frequent patterns, hidden patterns, themes, and the context in large datasets that are very easy to understand and interpret by data analysts and normal users [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms

Lakshika¹,

Caldera²

2022

Adv. sci. technol. eng. syst. j.

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Owing to technological development, the internet has become the world's largest platform where an unaccountable amount of e-news information is freely available to use. Most of the time, e-newspaper readers have to examine the massive collection of e-news articles to locate necessary information relevant to them. Massive semi-structured and unstructured texts usually mislead the readers when they search and understand data for some knowledge. Furthermore, manually reading a collection of e-news articles for some knowledge is tedious and unproductive. The literature related to Knowledge Discovery from text documents has had a substantial improvement in this regard and Association Rule Extraction using text documents, in particular, has become a more frequent and imperative research approach to finding out the most significant information, patterns, and features in the text documents while diminishing the time for reading all the documents. This study provides a comprehensive review of Association Rule extraction using textual data covering the essential topics; Pre-processing, steps in Association Rule Mining, and rule mining algorithms. Out of the various existing association rule mining algorithms, the two most important algorithms, Apriori and FP Growth, are chosen for the experiment using e-news articles. Based on the experimental results, this study discusses the performance, significant bottlenecks, recent breakthroughs of rule mining algorithms, and finally the perspective directions to facilitate future research.

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Section: Introductionmentioning

confidence: 99%

Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms

Lakshika¹,

Caldera²

2022

Adv. sci. technol. eng. syst. j.

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“…Support(s) were defined as the parts of record that come together X and Y to the total number of records in the dataset. Confidence was calculated as percentage of transactions that contain X and Y to the total number of records that contain X, where if the percentage exceeds of confidence threshold [29].…”

Section: Association Rulesmentioning

confidence: 99%

A Hybrid Approach of Association Rule & Hidden Makov Model to Improve Efficiency Medical Text Classification

Al-qozani¹,

AL-Wagih²

2018

IJCATR

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Abstract:Text classification problem is a set of documents be classified into a predefined set of categories, each document is classified based on a set of features (words). However, some of the words not relevant to a category which causes a gap between words relevance in a document. A lot of research articles in public databases, and The digitization of critical medical information such as lab reports, patients records, research papers, and anatomic images tremendous amounts of biomedical research data are generated every day. So that, the classification this data and retrieving information relevant to information users' needs have been a primary research issue in the field of Information Retrieval, and the adoption of classification has been applied to tackle this particular problem. In this paper, we propose a hybrid model for the classification of biomedical texts according to their content, using Association Rules and Hidden Markov Model as classifier. In order to demonstrate it, we present a set of experiments performed on OHSUMED biomedical text corpora. Our classifier compared with Naive Bayes and Support Vector Machine models. The evaluation result shows that the proposed classification is complete and accurate when compared with Naive Bayes and Support Vector Machine models.

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“…In contrast, there is a lack of works for discovering the association rules in text [11]. Association Rule Text Mining (ARTM) results in many practical applications, e.g., for building text classifiers [3,13].…”

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confidence: 99%

“…In the paper [8], association rules are used to derive a feature set from pre-classified text documents. Interestingly, the authors of a survey paper [11] discovered that the Apriori algorithm is also suitable for ARTM, and utilized mostly in various domains, especially in the domain of Medical Care [11].…”

mentioning

confidence: 99%

Population-based metaheuristics for Association Rule Text Mining

Fister

Deb

2020

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics &Amp; Swarm Intelligence

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Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper deals with the problem of Association Rule Text Mining. To solve the problem, the PSO-ARTM method was proposed, that consists of three steps: Text preprocessing, Association Rule Text Mining using population-based metaheuristics, and text postprocessing. The method was applied to a transaction database obtained from professional triathlon athletes' blogs and news posted on their websites. The obtained results reveal that the proposed method is suitable for Association Rule Text Mining and, therefore, offers a promising way for further development.

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A survey of association rule mining in text applications

Cited by 11 publications

References 8 publications

Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms

Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms

A Hybrid Approach of Association Rule & Hidden Makov Model to Improve Efficiency Medical Text Classification

Population-based metaheuristics for Association Rule Text Mining

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