Data Mining and Knowledge Discovery Handbook (Springer by Oded Maimon, Lior Rokach

By Oded Maimon, Lior Rokach

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This booklet organizes key ideas, theories, criteria, methodologies, tendencies, demanding situations and functions of knowledge mining and data discovery in databases. It first surveys, then presents finished but concise algorithmic descriptions of equipment, together with vintage tools plus the extensions and novel tools built lately. It additionally offers in-depth descriptions of knowledge mining purposes in quite a few interdisciplinary industries.
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Topics
Computational Intelligence
Data Mining and information Discovery
Control
Artificial Intelligence (incl. Robotics)

Extra info for Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

Example text

Different systems that use data stream mining techniques are also presented. Spatio-temporal - Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining, which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time.

The goal of this demonstration is to prove that these methods can be successfully used to identify outliers that constitute potential errors. The implementations are designed to work on larger data sets and without extensive amounts of domain knowledge. 1 Statistical Outlier Detection Outlier values for particular fields are identified based on automatically computed statistics. For each field, the mean and standard deviation are utilized, and based on Chebyshev’s theorem (Barnett and Lewis, 1994) those records that have values in a given field outside a number of standard deviations from the mean are identified.

427-438. Redman, T. The Impact of Poor Data Quality on the Typical Enterprise, Communications of the ACM 1998; 41(2):79-82. , Maimon, O. (2005), Clustering Methods, Data Mining and Knowledge Discovery Handbook, Springer, pp. 321-352. , Using Recon for Data Cleaning. In Advances in Knowledge Discovery and Data Mining, Fayyad, U. , eds. MIT Press/AAAI Press, 1995. , & Agrawal, R. Mining Association Rules with Item Constraints. Proceedings of SIGMOD International Conference on Management of Data; 1996 June; Montreal, Canada.

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