Lecture Notes in Data Mining

Michael W Berry

ISBN 10: 9812568026 / ISBN 13: 9789812568021

Edité par World Scientific Publishing Company Sep 2006, 2006

A propos de cet article

Neuware - The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited 'student-authored lectures' which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.

N° de réf. du vendeur 9789812568021

Détails bibliographiques

• Titre: Lecture Notes in Data Mining
• Éditeur: World Scientific Publishing Company Sep 2006
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 9812568026
• ISBN à 13 chiffres: 9789812568021
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 231 L x 161 H x 15 P (en millimètres) 231 millimètres de largeur par 161 millimètres de hauteur par 15 millimètres de profondeur
• Poids: 481 grammes

A propos de ce titre

Synopsis

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.