With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.
Heuristic and Optimization for Knowledge Discovery; A Heuristic Algorithm for Feature Selection Based on Optimization Techniques; Cost-Sensitive Classification using Decision Trees, Boosting and MetaCost; Heuristic Search-Based Stacking of Classifiers; Designing Component-Based Heuristic Search Engines for Knowledge Discovery; Clustering Mixed Incomplete Data; Bayesian Learning; The Role of Sampling in Data Mining; The Gamma Test; Neural Networks; How to Train Multilayer Perceptrons Efficiently with Large Data; Cluster Analysis of Marketing Data Examining On-Line Shopping; Heuristics in Medical Data Mining; Understanding Credit Card Users Behaviour; Heuristic Knowledge Discovery for Archaeological Data Using Cultural Algorithms and Rough Sets