Review:
Pattern Recognition Algorithms for Data Mining addresses totally different sample recognition (PR) duties in a unified framework with each theoretical and experimental outcomes. Tasks lined embrace information condensation, characteristic choice, case technology, clustering/classification, and rule era and analysis. This quantity presents varied theories, methodologies, and algorithms, utilizing each classical approaches and hybrid paradigms. The authors emphasize giant datasets with overlapping, intractable, or nonlinear boundary lessons, and datasets that display granular computing in smooth frameworks.
Organized into eight chapters, the e-book begins with an introduction to PR, information mining, and data discovery ideas. The authors analyze the duties of multi-scale knowledge condensation and dimensionality discount, then discover the issue of studying with help vector machine (SVM). They conclude by highlighting the importance of granular computing for various mining duties in a tender paradigm.
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