http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
Overviews on rule quality -- Choosing the Right Lens: Finding What is Interesting in Data Mining -- A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study -- Association Rule Interestingness Measures: Experimental and Theoretical Studies -- On the Discovery of Exception Rules: A Survey -- From data to rule quality -- Measuring and Modelling Data Quality for Quality-Awareness in Data Mining -- Quality and Complexity Measures for Data Linkage and Deduplication -- Statistical Methodologies for Mining Potentially Interesting Contrast Sets -- Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality -- Rule quality and validation -- A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link -- Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules -- Association Rule Interestingness: Measure and Statistical Validation -- Comparing Classification Results between N-ary and Binary Problems.
edited by Fabrice J. Guillet, Howard J. Hamilton.
Appl.Mathematics/Computational Methods of Engineering
Artificial Intelligence (incl. Robotics)
Studies in Computational Intelligence, 43