A survey on the use of association rules mining techniques in textual social media

Abstract

The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

“…In addition, confidence is the conditional probability of finding the item set of the consequence given the occurrence of the antecedent [7]. These definitions are consistent with a wide range of works in the field of ARM [28][29][30][31]. Another popular metric is the lift, which calculates whether the two item sets A and B of rule A → B are dependent or independent of each other [29]:…”

“…Indeed, practice vastly uses ARM algorithms. One significant advantage is their ability to deal with large amounts of unstructured data and that they provide very interpretable rules suitable to enhance decision-making [30]. For instance, [34] applies the FP-growth algorithm for PM sensor data of Internet of Things hardware to extract association rules.…”

“…For instance, [34] applies the FP-growth algorithm for PM sensor data of Internet of Things hardware to extract association rules. ARM can also be used on text data, even if one cannot apply it on raw text but on different representations like a bag of words or term frequency [30]. Bag of words and term frequency are typical text representations using NLP.…”

Mining Associations between Air Quality and Natural and Anthropogenic Factors

“…Association rule mining (also termed as market basket analysis) is a very useful approach in business intelligence and this approach has been successfully applied in recommendation systems for various business domains such as catalog design, commodity display arrangement, joint-sale promotion, cross marketing, and loss-leader strategy [33,34]. Association rule mining finds confident associations or strong correlation relationships among data items contained in a large transaction database.…”

An Efficient Hiding Method for Privacy Preserving Utility Mining

“…Pattern mining is a significant data mining task utilized mainly to reveal specific patterns in large collections of data. Based on the different needs in diverse fields and systems, the extracted patterns may be classed as association rules (ARs) [1], sequential patterns [2], frequent patterns [3], or high utility patterns [4]. Among these patterns, Frequent Pattern Mining (FPM) [3] has been a popular research area for many years as it can reveal frequent associations between objects and items in binary databases.…”