Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming

Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

[1]  Daniel Sánchez,et al.  Measuring the accuracy and interest of association rules: A new framework , 2002, Intell. Data Anal..

[2]  Mykola Pechenizkiy,et al.  Context-Aware Personal Route Recognition , 2011, Discovery Science.

[3]  Indre Zliobaite,et al.  Identifying Hidden Contexts in Classification , 2011, PAKDD.

[4]  Frans Coenen,et al.  Tree Structures for Mining Association Rules , 2004, Data Mining and Knowledge Discovery.

[5]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[6]  Mohammed J. Zaki.  Data Mining and Analysis: Fundamental Concepts and Algorithms , 2014 .

[7]  Cheng Wu,et al.  Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.

[8]  Sebastián Ventura,et al.  Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules , 2011, Knowledge and Information Systems.

[9]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[10]  Chengqi Zhang,et al.  ARMGA: IDENTIFYING INTERESTING ASSOCIATION RULES WITH GENETIC ALGORITHMS , 2005, Appl. Artif. Intell..

[11]  Gerhard Widmer,et al.  Learning in the presence of concept drift and hidden contexts , 2004, Machine Learning.

[12]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[13]  Mykola Pechenizkiy,et al.  Mining exceptional relationships with grammar-guided genetic programming , 2015, Knowledge and Information Systems.

[14]  Josep Domingo-Ferrer,et al.  Discrimination- and privacy-aware patterns , 2014, Data Mining and Knowledge Discovery.

[15]  Kotagiri Ramamohanarao,et al.  Enhancing Traditional Classifiers Using Emerging Patterns , 2013, Contrast Data Mining.

[16]  Patrick Brézillon,et al.  Context in problem solving: a survey , 1999, The Knowledge Engineering Review.

[17]  Sebastián Ventura,et al.  RM-Tool: A framework for discovering and evaluating association rules , 2011, Adv. Eng. Softw..

[18]  Peter D. Turney.  The Management of Context-Sensitive Features: A Review of Strategies , 2002, ArXiv.

[19]  Sen Zhang,et al.  New Techniques for Mining Frequent Patterns in Unordered Trees , 2015, IEEE Transactions on Cybernetics.

[20]  Peter A. Whigham,et al.  Grammar-based Genetic Programming: a survey , 2010, Genetic Programming and Evolvable Machines.

[21]  Alexander Tuzhilin,et al.  Comparing context-aware recommender systems in terms of accuracy and diversity , 2012, User Modeling and User-Adapted Interaction.

[22]  Sebastián Ventura,et al.  On the Use of Genetic Programming for Mining Comprehensible Rules in Subgroup Discovery , 2014, IEEE Transactions on Cybernetics.

[23]  Ding Zhenguo,et al.  An Improved FP-Growth Algorithm Based on Compound Single Linked List , 2009, 2009 Second International Conference on Information and Computing Science.

[24]  Mykola Pechenizkiy,et al.  Speeding-Up Association Rule Mining With Inverted Index Compression , 2016, IEEE Transactions on Cybernetics.

[25]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[26]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[27]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[28]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[29]  Mykola Pechenizkiy,et al.  Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? , 2012, Expert Syst. Appl..

[30]  Pedro M. Domingos.  Control-Sensitive Feature Selection for Lazy Learners , 1997, Artificial Intelligence Review.