Data Mining Concepts, Models, Methods, and Algorithms

Name: Data Mining Concepts, Models, Methods, and Algorithms
Price: 5.7 USD
Availability: InStock
ISBN: 9780471228523

ISBN-10: 0471228524

ISBN-13: 9780471228523

Edition: 2003

Authors: Mehmed Kantardzic

List price: $111.00

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Description:

Data mining describes the often complex and sophisticated tools used in automatic data analysis such as analyzing a customer's previous buying habits Emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning Describes representative state-of-the-art methods and algorithms originating from different disciplines Offers guidance on how and when to use a particular software tool from among the hundreds offered when faced with a data set to mine A Wiley-IEEE Press Publication To view the solutions manual, visit ftp://ftp.wiley.com/public/sci_tech_med/data_mining/

Book details

List price: $111.00
Copyright year: 2003
Publisher: John Wiley & Sons, Incorporated
Publication date: 10/25/2002
Binding: Paperback
Pages: 360
Size: 7.00" wide x 10.00" long x 0.75" tall
Weight: 1.342
Language: English



Preface


Data Mining Concepts


Introduction


Data-mining roots


Data-mining process


Large data sets


Data warehouses


Organization of this book


Review questions and problems


References for further study


Preparing the Data


Representation of raw data


Characteristics of raw data


Transformation of raw data


Missing data


Time-dependent data


Outlier analysis


Review questions and problems


References for further study


Data Reduction


Dimensions of large data sets


Features reduction


Entropy measure for ranking features


Principal component analysis


Values reduction


Feature discretization: ChiMerge technique


Cases reduction


Review questions and problems


References for further study


Learning from Data


Learning machine


Statistical learning theory


Types of learning methods


Common learning tasks


Model estimation


Review questions and problems


References for further study


Statistical Methods


Statistical inference


Assessing differences in data sets


Bayesian inference


Predictive regression


Analysis of variance


Logistic regression


Log-linear models


Linear discriminant analysis


Review questions and problems


References for further study


Cluster Analysis


Clustering concepts


Similarity measures


Agglomerative hierarchical clustering


Partitional clustering


Incremental clustering


Review questions and problems


References for further study


Decision Trees and Decision Rules


Decision trees


C4.5 Algorithm: generating a decision tree


Unknown attribute values


Pruning decision tree


C4.5 Algorithm: generating decision rules


Limitations of decision trees and decision rules


Associative-classification method


Review questions and problems


References for further study


Association Rules


Market-Basket Analysis


Algorithm Apriori


From frequent itemsets to association rules


Improving the efficiency of the Apriori algorithm


Frequent pattern-growth method


Multidimensional association-rules mining


Web mining


HITS and LOGSOM algorithms


Mining path-traversal patterns


Text mining


Review questions and problems


References for further study


Artificial Neural Networks


Model of an artificial neuron


Architectures of artificial neural networks


Learning process


Learning tasks


Multilayer perceptrons


Competitive networks and competitive learning


Review questions and problems


References for further study


Genetic Algorithms


Fundamentals of genetic algorithms


Optimization using genetic algorithms


A simple illustration of a genetic algorithm


Schemata


Traveling salesman problem


Machine learning using genetic algorithms


Review questions and problems


References for further study


Fuzzy Sets and Fuzzy Logic


Fuzzy sets


Fuzzy set operations


Extension principle and fuzzy relations


Fuzzy logic and fuzzy inference systems


Multifactorial evaluation


Extracting fuzzy models from data


Review questions and problems


References for further study


Visualization Methods


Perception and visualization


Scientific visualization and information visualization


Parallel coordinates


Radial visualization


Kohonen self-organized maps


Visualization systems for data mining


Review questions and problems


References for further study


References


Data-Mining Tools


Commercially and publicly available tools


Web site links


Data-Mining Applications


Data mining for financial data analysis


Data mining for the telecommunications industry


Data mining for the retail industry


Data mining in healthcare and biomedical research


Data mining in science and engineering


Pitfalls of data mining


Index


About the Author