Introduction to Data Mining

Pang-Ning Tan; Michael Steinbach; Vipin Kumar

ISBN 10: 0321420527 / ISBN 13: 9780321420527

Published by Pearson Education, 2005

About this Item

: Introducción a los conceptos y algoritmos fundamentales del 'data mining' o minería de datos, ideal para aquellos que se inician en este campo. Cada concepto se explora a fondo y se apoya con numerosos ejemplos. El texto requiere solo un conocimiento básico de matemáticas. Cada tema principal se organiza en dos capítulos, comenzando con conceptos básicos que proporcionan los antecedentes necesarios para comprender cada técnica de minería de datos, seguidos de conceptos y algoritmos más avanzados. EAN: 9780321420527 Tipo: Libros Categoría: Tecnología Título: Introduction to Data Mining Autor: Pang-Ning Tan| Michael Steinbach| Vipin Kumar Editorial: Pearson Education Idioma: en Páginas: 769 Formato: tapa blanda.

Seller Inventory # Happ-2026-08-19-91b43d3c

Bibliographic Details

• Title: Introduction to Data Mining
• Publisher: Pearson Education
• Publication Date: 2005
• Language: English
• ISBN 10: 0321420527
• ISBN 13: 9780321420527
• Binding: Soft cover
• Condition: As New

About this title

Synopsis

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.  

Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

 

 

Quotes

This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.

-Sanjay Ranka, University of Florida

 

In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules).

-Mohammed Zaki, Rensselaer Polytechnic Institute