Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.
This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
Data Mining has been identified as one of the ten emergent technologies of the 21st century (MIT Technology Review, 2001). This discipline aims at discovering knowledge relevant to decision making from large amounts of data. After some knowledge has been discovered, the final user (a decision-maker or a data-analyst) is unfortunately confronted with a major difficulty in the validation stage: he/she must cope with the typically numerous extracted pieces of knowledge in order to select the most interesting ones according to his/her preferences. For this reason, during the last decade, the designing of quality measures (or interestingness measures) has become an important challenge in Data Mining.
The purpose of this book is to present the state of the art concerning quality/interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include reviews, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included.