Java Data Mining: Strategy, Standard, and Practice
A Practical Guide for Architecture, Design, and Implementation
- 1st Edition - November 7, 2006
- 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
Purchase options
Institutional subscription on ScienceDirect
Request a sales quote- 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
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
- No. of pages: 544
- Language: English
- Edition: 1
- Published: November 7, 2006
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780123704528
- eBook ISBN: 9780080495910
MH
Mark F. Hornick
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.
EM
Erik Marcadé
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
SV