By Oded Maimon, Lior Rokach
This booklet organizes key ideas, theories, criteria, methodologies, traits, demanding situations and functions of knowledge mining and data discovery in databases. It first surveys, then offers complete 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 information mining functions in a variety of interdisciplinary industries.
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Additional resources for Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)
The second group of methods to handle missing attribute values, in which missing attribute values are taken into account during the main process of acquiring knowledge is represented, for example, by a modiﬁcation of the LEM2 (Learning from Examples Module, version 2) rule induction algorithm in which rules are induced form the original data set, with missing attribute values considered to be ”do not care” conditions or lost values. 5 (Quinlan, 1993) approach to missing attribute values is another example of a method from this group.
Beneﬁt analysis – to understand the effect of the potential KDD\DM results on the enterprise. Problem characteristics – analysis of the problem itself for its suitability to the KDD process. Mining complex objects of arbitrary type – Expanding Data Mining inference to include also data from pictures, voice, video, audio, etc. This will require adapting and developing new methods (for example, for comparing pictures using clustering and compression analysis). Temporal aspects - many data mining methods assume that discovered patterns are static.
Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm. Multi-label classiﬁcation - Most of the research in the ﬁeld of supervised learning has been focused on single label tasks, where training instances are associated with a single label from a set of disjoint labels. However, Textual data, such as documents and web pages, are frequently annotated with more than a single label. In Chapter 34, Grigorios Tsoumakas, Loannis Katakis and Loannis Vlahavas review techniques for addressing multi-label classiﬁcation task grouped into the two categories: i) problem transformation, and ii) algorithm adaptation.