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Practical Data Mining by Monte F Hancock Jr 2019 Paperback BRAND NEW
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Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN
- 9780367381905
- EAN
- 9780367381905
About this product
Product Identifiers
Publisher
Auerbach Publishers, Incorporated
ISBN-10
0367381907
ISBN-13
9780367381905
eBay Product ID (ePID)
18038297862
Product Key Features
Number of Pages
302 Pages
Publication Name
Practical Data Mining
Language
English
Publication Year
2019
Subject
Databases / Data Mining, Databases / General, Information Technology
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Weight
19.9 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
Table Of Content
What Is Data Mining and What Can It Do? The Data Mining Process. Problem Definition (Step 1). Data Evaluation (Step 2). Feature Extraction and Enhancement (Step 3). Prototyping Plan and Model Development (Step 4). Model Evaluation (Step 5). Implementation (Step 6). Supervised Learning Genre Section 1--Detecting and Characterizing Known Patterns. Forensic Analysis Genre Section 2--Detecting, Characterizing, and Exploiting Hidden Patterns. Genre Section 3--Knowledge: Its Acquisition, Representation, and Use.
Synopsis
Intended for those who need a practical guide to proven and up-to-date data mining techniques and processes, this book covers specific problem genres. With chapters that focus on application specifics, it allows readers to go to material relevant to their problem domain. Each section starts with a chapter-length roadmap for the given problem domain. This includes a checklist/decision-tree, which allows the reader to customize a data mining solution for their problem space. The roadmap discusses the technical components of solutions., Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in technical waters. Revealing the lessons known to the seasoned expert, yet rarely written down for the uninitiated, Practical Data Mining explains the ins-and-outs of the detection, characterization, and exploitation of actionable patterns in data. This working field manual outlines the what, when, why, and how of data mining and offers an easy-to-follow, six-step spiral process. Catering to IT consultants, professional data analysts, and sophisticated data owners, this systematic, yet informal treatment will help readers answer questions, such as: What process model should I use to plan and execute a data mining project? How is a quantitative business case developed and assessed? What are the skills needed for different data mining projects? How do I track and evaluate data mining projects? How do I choose the best data mining techniques? Helping you avoid common mistakes, the book describes specific genres of data mining practice. Most chapters contain one or more case studies with detailed projects descriptions, methods used, challenges encountered, and results obtained. The book includes working checklists for each phase of the data mining process. Your passport to successful technical and planning discussions with management, senior scientists, and customers, these checklists lay out the right questions to ask and the right points to make from an insider's point of view. Visit the book's webpage
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