New PDF release: Data Mining and Knowledge Discovery Handbook (Springer

New PDF release: Data Mining and Knowledge Discovery Handbook (Springer

By Oded Maimon, Lior Rokach

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This e-book organizes key ideas, theories, criteria, methodologies, traits, demanding situations and functions of knowledge mining and data discovery in databases. It first surveys, then offers accomplished but concise algorithmic descriptions of tools, together with vintage equipment plus the extensions and novel tools constructed lately. It additionally supplies in-depth descriptions of knowledge mining purposes in a number of interdisciplinary industries.
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Extra info for Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

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Data Quality and Systems Theory, Communications of the ACM 1998; 41(2):66-71. Raman, V. & Hellerstein, J. M. Potter’s wheel an interactive data cleaning system. Proceedings of 27th International Conference on Very Large Databases 2001 September 11-14; Rome, Italy. 381–391. , & Shim, K. Efficient Algorithms for Mining Outliers from Large Data Sets. Proceedings of ACM SIGMOD International Conference on Management of Data; 2000 Dallas. 427-438. Redman, T. The Impact of Poor Data Quality on the Typical Enterprise, Communications of the ACM 1998; 41(2):79-82.

In both disciplines there are methods to deal with missing attribute values. Some theoretical properties of data sets with missing attribute values were studied in (Imielinski and Lipski, 1984, Lipski, 1979, Lipski, 1981). In general, methods to handle missing attribute values belong either to sequential methods (called also preprocessing methods) or to parallel methods (methods in which missing attribute values are taken into account during the main process of acquiring knowledge). Sequential methods include techniques based on deleting cases with missing attribute values, replacing a missing attribute value by the most common value of that attribute, assigning all possible values to the missing attribute value, replacing a missing attribute value by the mean for numerical attributes, assigning to a missing attribute value the corresponding value taken from the closest fit case, or replacing a missing attribute value by a new vale, computed from a new data set, considering the original attribute as a decision.

Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining. Proceedings of 24th VLDB Conference; 1998 New York. 582–593. Lee, M. , Ling, T. , & Low, W. L. IntelliClean: a knowledge-based intelligent data cleaner. Proceedings of Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000 August 20-23; Boston, MA. 290-294. Levitin, A. & Redman, T. A Model of the Data (Life) Cycles with Application to Quality, Information and Software Technology 1995; 35(4):217-223. , Sung, S.

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