Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation
內容描述
Description
Whether you are a software developer, systems architect, data analyst, or
business analyst, if you want to take advantage of data mining in the
development of advanced analytic applications, Java Data Mining, JDM, the new
standard now implemented in core DBMS and data mining/analysis software, is a
key solution component. This book is the essential guide to the usage of the
JDM standard interface, written by contributors to the JDM standard.
The book discusses and illustrates how to solve real problems using
the JDM API. The authors provide you with:
Data mining introduction—an overview of data mining and the problems it
can address across industries; JDM’s place in strategic solutions to data
mining-related problems;
JDM essentials—concepts, design approach and design issues, with detailed
code examples in Java; a Web Services interface to enable JDM functionality in
an SOA environment; and illustration of JDM XML Schema for JDM objects;
JDM in practice—the use of JDM from vendor implementations and approaches
to customer applications, integration, and usage; impact of data mining on IT
infrastructure; a how-to guide for building applications that use the JDM API.
Free, downloadable KJDM source code referenced in the book available here
Table of Contents
Preface Guide to Readers Part I - Strategy 1. Overview of
Data Mining 1.1. Why is data mining relevant today? 1.2. Introducing
Data Mining 1.3. The Value of Data Mining 1.4. Summary 1.5.
References 2. Solving Problems in Industry 2.1. Cross-industry
data mining solutions 2.2. Data Mining in Industries 2.3. Summary
2.4. References 3. Data Mining Process 3.1. A standardized
data mining process 3.2. Data Analysis and Preparation…a more detailed
view 3.3. Data mining modeling, analysis, and scoring processes 3.4.
The Role of databases and data warehouses in Data Mining 3.5. Data mining
in enterprise software architectures 3.6. Advances in automated data
mining 3.7. Summary 3.8. References 4. Mining Functions and
Algorithms 4.1. Data mining functions 4.2. Classification 4.3.
Regression 4.4. Attribute Importance 4.5. Association 4.6.
Clustering 4.7. Summary 4.8. References 5. JDM Strategy
5.1. What is the JDM strategy? 5.2. Role of Standards 5.3. Summary
5.4. References 6. Getting Started 6.1. Business Understanding
6.2. Data Understanding 6.3. Data Preparation 6.4. Modeling
6.5. Evaluation 6.6. Deployment 6.7. Summary 6.8. References
Part II - Standard 7. Java Data Mining Concepts 7.1.
Classification problem 7.2. Regression problem 7.3. Attribute
importance 7.4. Association rules problem 7.5. Clustering problem
7.6. Summary 7.7. References 8. Design of the JDM API 8.1.
Object Modeling of Data Mining Concepts 8.2. Modular Packages 8.3.
Connection Architecture 8.4. Object Factories 8.5. URI for Datasets
8.6. Enumerated Types 8.7. Exceptions 8.8. Discovering DME
Capabilities 8.9. Summary 8.10. References 9. Using the JDM
API 9.1. Connection Interfaces 9.2. Using JDM Enumerations 9.3.
Using data specification interfaces 9.4. Using classification interfaces
9.5. Using Regression interfaces 9.6. Using Attribute Importance
interfaces 9.7. Using Association interfaces 9.8. Using Clustering
interfaces 9.9. Summary 9.10. References 10. XML Schema
10.1. Overview 10.2. Schema Elements 10.3. Schema Types 10.4.
Using PMML with the JDM Schema 10.5. Use cases for JDM XML Schema and
Documents10.6. Summary 10.7. References 11. Web Services
11.1. What is a Web Service? 11.2. Service Oriented Architecture (SOA)
11.3. JDM Web Service (JDMWS) 11.4. Enabling JDM Web Services using
JAX-RPC 11.5. Summary 11.6. References Part III - Practice
- Practical Problem Solving 12.1. Business Scenario 1: Targeted
Marketing Campaign 12.2. Business Scenario 2: Understanding Key Factors
12.3. Business Scenario 3: Using Customer Segmentation 12.4. Summary
12.5. Bibliography 13. Building Data Mining Tools using JDM
13.1. Data mining tools 13.2. Administrative Console 13.3. User
Interface to build and save a model13.4. User Interface to test model
quality 13.5. Summary 14. Getting Started with JDM Web Services
14.1. A Web Service client in PhP 14.2. A Web Service client in Java
14.3. Summary 14.4. References 15. Impacts on IT
Infrastructure 15.1. What does Data Mining require from IT? 15.2.
Impacts on computing hardware 15.3. Impacts on data storage hardware
15.4. Data access 15.5. Backup and recovery 15.6. Scheduling
15.7. Workflow 15.8. Summary 15.9. References 16. Vendor
implementations 16.1. Oracle Data Mining 16.2. KXEN (Knowledge
eXtraction ENgines) 16.3. Process for new Vendors 16.4. Process for
new JDM users 16.5. Summary 16.6. References Part IV. Wrapping
Up 17. Evolution of Data Mining Standards 17.1. Data Mining Standards
17.2. Java Community Process 17.3. Why so many standards? 17.4.
Where data mining standards have been and where will they go? 17.5.
Directions for data mining standards 17.6. Summary 17.7. References - Preview of Java Data Mining 2.0 18.1. Transformations
18.2. Time Series 18.3. Apply for Association 18.4. Feature
Extraction 18.5. Statistics 18.6. Multi-target Models 18.7. Text
Mining 18.8. Summary 18.9. References 19. Summary App.
A. Further Reading App. B. Glossary
