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Studies in Fuzziness and Soft Computing

By: Ashish Ghosh (Editor)

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Hardcover | 18 October 2004

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Hardcover
288 Pages

Dimensions(cm)
22.86 x 15.88 x 1.91

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Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Machine Learning Mathematics Applied Mathematics Engineering & Technology Other Technologies & Applied Sciences

Mining Technology & Engineering Technology in General Engineering in General

Other Editions and Formats

Paperback

Published: 15th November 2014

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Evolutionary Algorithms for Data Mining and Knowledge Discovery
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining
GAP: Constructing and Selecting Features with Evolutionary Computing
Multi-Agent Data Mining Using Evolutionary Computing
A Rule Extraction System with Class-Dependent Features
Knowledge Discovery in Data Mining via an Evolutionary Algorithm
Diversity and Neuro-Ensemble
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets
Evolutionary Computation in Intelligent Network Management
Genetic Programming in Data Mining for Drug Discovery
Microarray Data Mining with Evolutionary Computation
An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts
Table of Contents provided by Publisher. All Rights Reserved.

Studies in Fuzziness and Soft Computing

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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