Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.