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Quality Measures in Data Mining

Quality Measures in Data Mining


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About the Book

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.


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Product Details
  • ISBN-13: 9783642079528
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
  • Binding: Paperback
  • Height: 234 mm
  • No of Pages: 314
  • Series Title: Studies in Computational Intelligence
  • Weight: 512 gr
  • ISBN-10: 3642079520
  • Publisher Date: 18 Nov 2010
  • Edition: 1st ed. Softcover of orig. ed. 2007
  • Language: English
  • Returnable: Y
  • Spine Width: 18 mm
  • Width: 156 mm


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