Download Advanced Techniques in Knowledge Discovery and Data Mining by Nikhil Pal PDF

By Nikhil Pal

ISBN-10: 1852338679

ISBN-13: 9781852338671

Transparent and concise reasons to appreciate the educational paradigms. Chapters written through top international specialists.

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Extra resources for Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing)

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As a feasibility study, data with known but nonobvious information content prove that the methods can indeed help in rapidly detecting the desired information. , interactive multivariate data visualization. In this regard, the chapter is as organized as follows. In the next section, the general data acquisition process and the chosen instance data for the conducted experiments are described. In the following section, the spectrum of applied methods and their tool implementations are covered. Then the conducted experiments and the achieved results are presented and discussed.

Sequence of operations (called a recipe) within the tool. It is now common to have so-called cluster tools comprising of multiple stations capable of running a variety of processes, so a recipe can be quite complex. While the process record has been moved into an electronic database, it has also been expanded over time to contain more data. Measurement equipment will generally store results to a dedicated database before a result summary is attached to the process record. Additionally there are separate databases dedicated to certain tools or tool groups for recipe repositories and recording events and in situ measurements during processing.

Methods based on the statistical information of a single variable or the correlation of two selected variables. Thus, for the cases regarded earlier, advanced methods from soft computing originating from the fields of pattern recognition, neural networks, bioinspired computing and statistics, and corresponding tool implementations provide improved leverage by multivariate, nonparametric, and nonlinear approaches. 1, specific methods and their potential for advanced process window modeling and detection of deviation from the process window in (semi)automatic operation are briefly presented.

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