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

Mining for knowledge to build decision support system for diagnosis and treatment of tinnitus

Tinnitus problems affect a significant portion of the population and are difficult to treat. Treatment processes are plentiful, yet not completely understood. In this dissertation, we present a knowledge discovery approach which can be used to build a decision support system for supporting tinnitus treatment. Our approach is based on a significant enlargement of the initial tinnitus database by adding many new tables containing new temporal features related to tinnitus evaluation and treatment outcome. Research presented in this thesis includes knowledge discovery with temporal, text, and quantitative data from a patient dataset of 3013 visits representing 758 unique patient tuples. Additionally, a new rule generating technique and clustering methods are presented and used to develop additional new temporal features and knowledge in this complex domain. Of particular interest is the role that emotions play in treatment success for tinnitus following the TRT method developed by Dr. Pawel Jastreboff. The ultimate goal of understanding the relationships among the treatment factors and measurements in order to better understand tinnitus treatment will result in the design foundations of a decision support system to aid in tinnitus treatment effectiveness

Action Rules Discovery Based on Tree Classifiers and Meta-actions

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. To our knowledge, all these algorithms assume that all attributes are symbolic or require prior discretization of all numerical attributes. This paper presents a new approach for generating action rules from datasets with numerical attributes by incorporating a tree classifier and a pruning step based on metaactions. Meta-actions are seen as a higher-level knowledge (provided by experts) about correlations between different attributes