Quality Measures in Data Mining

• Corporate Author: SpringerLink (Online service)
• Other Authors: Guillet, Fabrice J. (Editor), Hamilton, Howard J. (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Series: Studies in Computational Intelligence, 43
• Subjects: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

Table of Contents:

• 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.