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.

本文提出了一种基于规则的机器学习方法的问题，以发现单个智能手机用户的行为规则以提供情境感知的智能服务。如今，智能手机被认为是提供各种上下文感知的个性化服务的最重要的物联网（IoT）设备之一。这些设备可以记录个人的上下文数据，例如时间，空间或社交上下文，以及他们的日常行为活动记录。关联规则挖掘（ARM）是最流行的基于规则的机器学习方法，用于利用给定的数据集发现特定约束偏好的规则。但是，它会生成许多无趣的上下文关联，从而导致产生大量的冗余在做出上下文感知决策中变得无用的规则。这种冗余的生成不仅使规则集不必要地变大，而且使上下文感知的决策过程更加复杂和无效。为了最大程度地减少这些问题，在本文中，我们提出了一种基于规则的机器学习方法“ ABC-RuleMiner”，该方法可以有效地识别关联中的冗余，并通过以下方式为单个用户发现一组非冗余行为规则 （IF-THEN）：考虑到优先级相关上下文。我们对个人上下文智能手机数据集的实验表明，与传统的基于规则的方法相比，此规则发现方法更有效。