Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Quality Measures in Data Mining

Quality Measures in Data Mining

Hamilton, Howard J.
Guillet, Fabrice

Paperback

Series: Studies in Computational Intelligence, Book 43

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 3642079520
ISBN13: 9783642079528
Publisher: Springer Nature
Published: Nov 18 2010
Pages: 314
Weight: 1.01
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English
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.

Also in

General Mathematics

View All