Information Cartography in Association Rule Mining
TLDR
In this article , the authors developed a method for automatic creation of metro maps of information obtained by Association Rule Mining and, thus, spread its applicability to the other machine learning methods.Abstract:
Association Rule Mining is a machine learning method for discovering the interesting relations between the 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 present this automatically 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 several problem domains, where pattern mining was necessary. The aim of this study is to develop a method for automatic creation of metro maps of information obtained by Association Rule Mining and, thus, spread its applicability to the other machine learning methods. 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 that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users. read more
Citations
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An Optimized Sanitization Approach for Minable Data Publication
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TL;DR: Wang et al. as discussed by the authors proposed an optimized sanitization approach for minable data publication (named as SA-MDP), which supports association rules mining function while providing privacy protection for specific rules.
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Minable Data Publication Based on Sensitive Association Rule Hiding
TL;DR: Wang et al. as discussed by the authors developed a customized multi-objective evolutionary algorithm (MOEA) to solve the local optimum trapping issue and slow convergence speed issue, which can balance the trade-off between data privacy and data utility.
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A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence
TL;DR: In this paper , a supervised learning-based approach is proposed to predict potential technology convergence by using the link prediction results, the technological influence relationships, and the technological relevance between technology classes.
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Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure
TL;DR: Zhang et al. as mentioned in this paper proposed FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems.
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A comprehensive review of visualization methods for association rule mining: Taxonomy, challenges, open problems and future ideas
TL;DR: Association rule mining is intended for searching for the relationships between attributes in transaction databases as discussed by the authors , and the whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out.
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