Information Cartography in Association Rule Mining

Abstract

Association Rule Mining is a data mining method for discovering the interesting relations between attributes in a huge transaction database. Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suitable for the user. Recently, an information cartography has been proposed for creating structured summaries of information and visualizing with methodology called "metro maps". This was applied to many problem domains. In the hope of widening its applicability domain, the aim of this study is to develop a method for the automatic creation of metro maps of information obtained by association rule mining. Although the proposed method consists of multiple steps, its core presents metro map construction that is defined in the study as an optimization problem, which is solved using an evolutionary algorithm. Finally, this was applied to four well-known UCI Machine Learning datasets and one sport dataset. Visualizing the resulted metro maps not only justifies the fact this is a suitable tool for presenting structured knowledge hidden in data, but also that they can even tell stories to users.

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

“…Therefore, we apply association rule mining to generate directional connection rules between them. Association rule mining, one of the representative unsupervised learning techniques, is the process of revealing important hidden relationships among sets of items in a huge database [51]. Let  = {  ,   ,   , … ,   } be a set of items, and  and  are two subsets of , association rule mining generates rules in the form of  → , where  ∩  = ∅ [52].…”

An Optimized Sanitization Approach for Minable Data Publication

“…The SOEA-based method has shown its superiority to obtain the optimal solution than traditional methods [26,27] . For example, a cuckoo search optimization algorithm is adopted for deleting/inserting items from/into the dataset, and a solution with the fewest sideeffects is obtained [12] .…”

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

“…In recent years, specifc constraints-based sequential pattern mining has been paid much attention. Sequential association rule mining [17,18] looks up association rules in transactional data. It does not consider the sequence of items but focuses on the fact that there is an intersection between the front and back itemsets.…”

“…Fisher's test statistic is a frequently used statistic value in the traditional permutation testing [15][16][17]. Its calculation process is based on the 2 × 2 contingency table which is known in Table 1.…”