Chapter: Multi-Agent Data Mining using Evolutionary Computing

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Home > Evolutionary Computation in Data Mining > Multi-Agent Data Mining using Evolutionary Computing

Evolutionary Computation in Data Mining |

Ashish Ghosh

Springer Berlin Heidelberg

Multi-Agent Data Mining using Evolutionary Computing

Chapter Info

Authors

Riyaz Sikora

ARI Id

1664775158385_3168000

Access

Not Available Free

Pages

57-78

DOI

10.1007/3-540-32358-9_4

Chapter URL

https://rd.springer.com/chapter/10.1007/3-540-32358-9_4

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		I-XVII
Evolutionary Algorithms for Data Mining and Knowledge Discovery	Ashish Ghosh	1-20
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining	Jose Ramon Cano; Francisco Herrera; Manuel Lozano	21-39
GAP: Constructing and Selecting Features with Evolutionary Computing	Matthew G. Smith; Larry Bull	41-56
Multi-Agent Data Mining using Evolutionary Computing	Riyaz Sikora	57-78
A Rule Extraction System with Class-Dependent Features	Xiuju Fu; Lipo Wang	79-99
Knowledge Discovery in Data Mining via an Evolutionary Algorithm	Qi Yu; Kay Chen Tan; Tong Heng Lee	101-123
Diversity and Neuro-Ensemble	Minh Ha Nguyen; Hussein Abbass; Robert McKay	125-156
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets	Olfa Nasraoui; Elizabeth Leon; Raghu Krishnapuram	157-188
Evolutionary Computation in Intelligent Network Management	Ajith Abraham	189-210
Genetic Programming in Data Mining for Drug Discovery	W. B. Langdon; S. J. Barrett	211-235
Microarray Data Mining with Evolutionary Computation	Gary B. Fogel	237-248
An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts	Po-Chang (P.C.) Ko; Ping-Chen (P.C.) Lin	249-263
Back Matter		264-265
Chapters/Headings	Author(s)	Pages	Info

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