Java Data Mining: Strategy, Standard, and Practice
A Practical Guide for Architecture, Design, and Implementation
- 1st Edition - November 7, 2006
- Latest edition
- Authors: Mark F. Hornick, Erik Marcadé, Sunil Venkayala
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 3 7 0 4 5 2 - 8
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 4 9 5 9 1 - 0
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… Read more
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.
- 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
This book is for software developers and applications architects interested in or who need data mining analysis as part of their application. It can be used by both novice and advanced java developers as a reference for incorporating data mining into applications, leveraging the sample code provided. For example, a Java developer may know he wants to classify a customer's interest in a product, but doesn't know how to get started. This book provides a quick start for using data mining in a practical context. On the other hand, experienced data miners who use Java will also gain benefits by seeing working code of how to use JSM to accomplish mining task
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 Documents
10.6. 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
12. 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 model
13.4. 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
18. 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
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 Documents
10.6. 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
12. 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 model
13.4. 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
18. 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
- Edition: 1
- Latest edition
- Published: November 7, 2006
- Language: English
MH
Mark F. Hornick
Mark Hornick has lead the Java Data Mining (JSR-73) expert group since its inception in July of 2000, and now leads the JSR-247 expert group working towards JDM 2.0. Mr. Hornick brings nearly 20 years experience in the design and implementation of advanced distributed systems, including in-database data mining, distributed object management, and Java APIs. Mr. Hornick is a senior manager in Oracle’s Data Mining Technologies group.
Mr. Hornick joined Oracle through Oracle’s acquisition of Thinking Machines Corporation in 1999. Prior to Thinking Machines, where he served as architect for TMC’s next generation data mining software, Mr. Hornick was a Principal Investigator at GTE Laboratories, involved in advanced telecommunications network management software, distributed transaction management research, and distributed object management research.
Mr. Hornick has contributed to several other data mining standards, including the Data Mining Group’s PMML, ISO SQL/MM for Data Mining, and the Object Management Group’s Common Warehouse Metadata. He has given talks at the International Conference on Knowledge Discovery and Databases, JavaOne, JavaPro Live!, and The ServerSide Symposium on data mining standards and JDM. He has also published various papers and articles over his career.
Mr. Hornick holds a bachelor degree from Rutgers University in Computer Science, and a masters degree from Brown University, also in Computer science where he specialized in distributed object databases.
Affiliations and expertise
Sr. Manager, Data Mining Technologies, Oracle Corporation, Burlington, MAEM
Erik Marcadé
With over 17 years of experience in the neural network industry, Erik Marcade, founder and chief technical officer for KXEN, is responsible for software development and information technologies. Prior to founding KXEN, Mr. Marcade developed real-time software expertise at Cadence Design Systems, accountable for advancing real-time software systems as well as managing “system-on-a-chip” projects. Before joining Cadence, Mr. Marcade spearheaded a project to restructure the marketing database of the largest French automobile manufacturer for Atos, a leading European information technology services company.
In 1990, Mr. Marcade co-founded Mimetics, a French company that processes and sells development environment, optical character recognition (OCR) products and services using neural network technology.
Prior to Mimetics, Mr. Marcade joined Thomson-CSF Weapon System Division as a software engineer and project manager working on the application of artificial intelligence for projects in weapons allocation, target detection and tracking, geo-strategic assessment, and software quality control. He contributed to the creation of Thomson Research Laboratories in Palo Alto, CA (Pacific Rim Operation-PRO) as senior software engineer. There he collaborated with Stanford University on the automatic landing and flare system for Boeing, and Kestrel Institute, a non-profit computer science research organization. He returned to France to head Esprit projects on neural networks development.
Mr. Marcade holds an engineering degree from Ecole de l’Aeronautique et de l’Espace, specializing in process control, signal processing, computer science, and artificial intelligence
Affiliations and expertise
Founder and Chief Technical Officer, KXEN, Paris, FranceSV
Sunil Venkayala
J2EE and XML group leader and Principal Member of Technical Staff at Oracle Data Mining Technologies. Expert group member of Java Data Mining (JDM) standard developed under JSR-73. More than five years experience in developing applications using predictive technologies available in the Oracle Database. More than seven years of experience in working with Java and Internet technologies. Authored JDM article in Java Developer Journal. Holds a B.S in Engineering and Masters in Industrial Management from Indian Institute Of Technology, Kanpur.
Affiliations and expertise
Principal Member of Technical Staff, Oracle, Burlington, MARead Java Data Mining: Strategy, Standard, and Practice on ScienceDirect