Predictive Data Mining: A Practical Guide Paperback - 1997
by Weiss, Sholom M.; Indurkhya, Nitin
- Used
- Good
- Paperback
This book presents a unified view of data mining, drawing from statistics, machine learning, and databases and focuses on the preparation of data and the development of an overall problem-solving strategy. It will interest researchers, programmers, and developers in knowledge discovery and data mining in the disciplines of AI, software engineering, and databases.
A$11.06
Free Delivery within USA
Standard delivery: 4 to 8 days
More delivery options
Standard delivery: 4 to 8 days
Details
- Title Predictive Data Mining: A Practical Guide
- Author Weiss, Sholom M.; Indurkhya, Nitin
- Binding Paperback
- Edition [ Edition: first
- Condition Used - Good
- Pages 228
- Volumes 1
- Language ENG
- Publisher Morgan Kaufmann Publishers, San Francisco
- Publication date 1997
- Bookseller's Inventory # G1558604030I3N00
- ISBN 9781558604032 / 1558604030
- Weight 0.86 lbs (0.39 kg)
- Dimensions 9 x 6 x 0.51 in (22.86 x 15.24 x 1.30 cm)
- Category Computers - Data Base Management
- Library of Congress subjects Database management, Data mining
- Library of Congress Catalogue Number 97030682
- Dewey Decimal Code 001.422
- Quantity available 1
About ThriftBooks Washington, United States
Biblio member since 2018
From the largest selection of used titles, we put quality, affordable books into the hands of readers
Reader reviews for Predictive Data Mining: A Practical Guide
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
First line
Data mining is the search for valuable information in large volumes of data.
