Robust and cost-effective approach for discovering action rules

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method

Object-driven and temporal action rules mining

In this thesis, I present my complete research work in the field of action rules, more precisely object-driven and temporal action rules. The drive behind the introduction of object-driven and temporally based action rules is to bring forth an adapted ap- proach to extract action rules from a subclass of systems that have a specific nature, in which instances are observed from assumingly different distributions (defined by an object attribute), and in which each instance is coupled with a time-stamp. In previous publications, we proposed an object-independency assumption that suggests extracting patterns from subsystems defined by unique objects, and then aggregat- ing similar patterns amongst all objects. The motivation behind this approach is based on the fact that same-object observations share similar features that are not shared with other objects, and these features are possibly not explicitly included in our dataset. Therefore, by individualizing objects prior to calculating action rules, variance is reduced, and over-fitting is potentially avoided. In addition to the object- independency assumption, temporal information is exploited by taking into account only the state transitions that occurred in the valid direction.j The common nature of object-driven and temporal action rules made us believe that this work is general enough to solve a diverse fields of areas where it is highly needed. In our case study, we show how our approach was applied to an information system of hypernasality patients; our results were further investigated by physicians collaborators to confirm them

Mining actionable patterns by role models

Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user. ”Actionability ” addresses this problem in that a pattern is deemed actionable if the user can act upon it in her favor. We introduce the notion of “action ” as a domain-independent way to model the domain knowledge. Given a data set about actionable features and an utility measure, a pattern is actionable if it summarizes a population that can be acted upon towards a more promising population observed with a higher utility. We present several pruning strategies taking into account the actionability requirement to reduce the search space, and algorithms for mining all actionable patterns as well as mining the top k actionable patterns. We evaluate the usefulness of patterns and the focus of search on a real-world application domain