Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach

Abstract

Recently, association rule mining plays a vital role in knowledge discovery in database. In fact, in most cases, the real datasets lead to a very large number of rules, which do not allow users to make their own selection of the most relevant. The difficult task is mining useful and nonredundant rules. Several approaches have been proposed, such as rule clustering, informative cover method and quality measurements. Another way to selecting relevant association rules, we believe that it is necessary to integrate a decisional approach within the knowledge discovery process. Therefore, in this paper, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis. In other side, the general process of association rules extraction becomes more and more complex, to solve such problem, we also proposed a multi-agent system for modeling the different process of our proposed approach. Therefore, we conclude our work by an empirical study applied to a set of banking data to illustrate the performance of our approach.

An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

“…However, these algorithms produce a large number of rules, preventing decision makers from making their own selection of the most interesting rules. To solve this problem, the integration of multi-criteria decision analysis approach is useful in practice for decision makers affected by redundancy in the extracted rules [29][30][31]. In this context, we use the ELECTRE TRI method, considering a set of extracted rules as the alternatives and support, confidence and lift as the criteria.…”

“…In the data mining field, the association rule algorithms produce Pγ,a large number of extracted rules that do not allow an expert to make their own selection of the most interesting. To deal with this problem, the integration of MCDA, and particularly the existing method known as ELECTRE TRI, offers the ability to sort the results [26][27][28][29].…”

Intelligent Transport System for Road Safety Based Data Mining Approach

“…This figure illustrates the itemsets by frequency, the result is sensitive to the minimum support introduced in the first step of Apriori algorithm. The second step is to generate the association rules from frequent itemsets previously extracted [26][27][28]. To visualize the extracted rules we used arulesViz [29] as an Rpackage extension, which implements several known and novel visualization techniques such as the matrix, group and graphs based visualization.…”

Selecting Relevant Association Rules From Imperfect Data

“…As stated in the introduction, our goal is threefold: (i) to enrich the expressivity of existing proposed frameworks dedicated to ARM with imperfect data, (ii) to complement them with a richer procedure for selecting relevant rules (rule pruning), and (iii) to present a simple way to incorporate domain knowledge to ease the mining process, and to help identifying relevant rules for a domain of interest. 3 Rule pruning. Most of the approaches use thresholds to select rules -only using support and confidence most often allows drastically reducing the number of rules in traditional ARM [1].…”