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ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services
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DOI：10.1016/j.jnca.2020.102762.png)
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Abstract
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This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data - for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method ABC-RuleMiner that effectively identifies the redundancy in associations, and discovers a set of non-redundant behavioral rules (IF-THEN) for individual users by taking into account the precedence of relevant contexts. Our experiments on individuals' contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.
Keywords:
Machine learning
Association rule mining
Non-redundancy
Context-aware computing
Decision making
Personalization
Mobile data analytics
Rule-based system
Intelligent services
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