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Java Data Mining Strategy Standard and Practice A Practical Guide for architecture design and implementation 1st Edition by Mark Hornick, Sunil Venkayala, Erik Marcadé ISBN 978-0123704528 0123704529

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Java Data Mining Strategy Standard and Practice A Practical Guide for architecture design and implementation 1st Edition Mark F. Hornick Digital Instant Download

Author(s): Mark F. Hornick, Erik Marcadé, Sunil Venkayala
ISBN(s): 9780123704528, 0123704529
Edition: 1
File Details: PDF, 7.35 MB
Year: 2006
Language: english
SKU: EB-977472 Category: Tags: , ,

Java Data Mining Strategy Standard and Practice A Practical Guide for architecture design and implementation 1st Edition by Mark F. Hornick, Sunil Venkayala,  Erik Marcadé – Ebook PDF Instant Download/Delivery: 978-0123704528, 0123704529
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Product details: 

ISBN 10:  0123704529

ISBN 13: 978-0123704528

Author:  Mark F. Hornick, Sunil Venkayala,  Erik Marcadé

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.

Table of contents: 

  1. Preface

  2. Guide to Readers

Part I – Strategy

  1. Chapter 1 – Overview of Data Mining
    3.1. Why Data Mining Is Relevant Today?
    3.2. Introducing Data Mining
    3.2.1. Data Mining by Other Names
    3.2.2. Data Mining Versus Other Forms of Advanced Analytics
    3.2.3. Process
    3.2.4. What Is a Data Mining Model?
    3.2.5. Some Jargon
    3.3. The Mining Metaphor
    3.4. The Value of Data Mining
    3.4.1. How Reliable Is Data Mining?
    3.4.2. How Can Data Mining Increase Profits and Reduce Costs?
    3.5. Summary
    3.6. References

  2. Chapter 2 – Solving Problems in Industry
    4.1. Cross-Industry Data Mining Solutions
    4.1.1. Customer Acquisition
    4.1.2. Customer Retention
    4.1.3. Response Modeling
    4.1.4. Fraud Detection
    4.1.5. Cross-Selling

  1. Chapter 3 – Data Mining Process
    5.1. A Standardized Data Mining Process
    5.1.1. Business Understanding Phase
    5.1.2. Data Understanding Phase
    5.1.3. Data Preparation Phase
    5.1.4. Modeling Phase
    5.1.5. Evaluation Phase
    5.1.6. Deployment Phase
    5.2. A More Detailed View of Data Analysis and Preparation
    5.3. Data Mining Modeling, Analysis, and Scoring Processes
    5.3.1. Model Building
    5.3.2. Model Apply
    5.3.3. Model Test
    5.4. The Role of Databases and Data Warehouses in Data Mining
    5.5. Data Mining in Enterprise Software Architectures
    5.5.1. Architectures
    5.5.2. Incorporating Data Mining into Business Operations
    5.5.3. Business Workflow
    5.6. Advances in Automated Data Mining
    5.7. Summary
    5.8. References

  2. Chapter 4 – Mining Functions and Algorithms
    6.1. Data Mining Functions
    6.2. Classification

  1. 6.3. Regression
    6.4. Attribute Importance
    6.5. Association
    6.6. Clustering
    6.7. Summary
    6.8. References

  2. Chapter 5 – JDM Strategy
    7.1. What Is the JDM Strategy?
    7.2. Role of Standards
    7.2.1. Why Create a Standard?
    7.2.2. What Do Data Mining Standards Enable?
    7.3. Summary
    7.4. References

  3. Chapter 6 – Getting Started
    8.1. Business Understanding
    8.2. Data Understanding
    8.3. Data Preparation
    8.4. Modeling
    8.4.1. Build
    8.4.2. Test
    8.5. Evaluation
    8.6. Deployment
    8.7. Summary
    8.8. References

Part II – Standards

  1. Chapter 7 – Java Data Mining Concepts
    9.1. Classification Problem
    9.1.1. Problem Definition: How to Reduce Customer Attrition?
    9.1.2. Solution Approach: Predict Customers Who Are Likely to Attrite
    9.1.3. Data Specification: CUSTOMERS Dataset
    9.1.4. Specify Settings: Fine-Tune the Solution to the Problem
    9.1.5. Select Algorithm: Find the Best Fit Algorithm
    9.1.6. Evaluate Model Quality: Compute Classification Test Metrics
    9.1.7. Apply Model: Obtain Prediction Results

  1. 9.2. Regression Problem
    9.2.1. Problem Definition: How to Reduce Processing Time of Residential Real-Estate Appraisals?
    9.2.2. Solution Approach: Property Value Prediction Using Regression
    9.2.3. Data Specification: REAL_ESTATE_APPRAISALS Dataset
    9.2.4. Select Algorithm: Find the Best Fit Algorithm
    9.2.5. Evaluate Model Quality: Compute Regression Test Metrics
    9.2.6. Apply Model: Obtain Prediction Results

    9.3. Attribute Importance
    9.3.1. Problem Definition: How to Find Important Customer Attributes?
    9.3.2. Solution Approach: Rank Attributes According to Predictive Value
    9.3.3. Data Specification, Fine-Tune Settings, and Algorithm Selection
    9.3.4. Use Model Details: Explore Attribute Importance Values

    9.4. Association Rules Problem
    9.4.1. Problem Definition: How to Identify Cross-Sell Products for Customers?
    9.4.2. Solution Approach: Discover Product Associations From Customer Data
    9.4.3. Data Specification: CUSTOMERS and Their Product Purchase Data
    9.4.4. Fine-Tune Settings: Filter Rules Based on Rule Quality Metrics
    9.4.5. Use Model Content: Explore Rules From the Model

    9.5. Clustering Problem
    9.5.1. Problem Definition: How to Understand Customer Behavior and Needs?
    9.5.2. Solution Approach: Find Clusters of Similar Customers
    9.5.3. Data Specification and Settings
    9.5.4. Use Model Details: Explore Clusters
    9.5.5. Apply Clustering Model: Assign New Cases to the Clusters

    9.6. Summary
    9.7. References

  2. Chapter 8 – Design of the JDM API
    10.1. Object Modeling of Data Mining Concepts
    10.1.1. Data Specification Objects

  1. 10.1.2. Settings Objects
    10.1.3. Models
    10.1.4. Test Metrics
    10.1.5. Tasks
    10.2. Modular Packages
    10.3. Connection Architecture
    10.4. Object Factories
    10.5. Uniform Resource Identifiers for Datasets
    10.6. Enumerated Types
    10.7. Exceptions
    10.8. Discovering DME Capabilities
    10.9. Summary
    10.10. References

  2. Chapter 9 – Using the JDM API
    11.1. Connection Interfaces
    11.1.1. Using the ConnectionFactory Interface
    11.1.2. Using the Connection Interface
    11.1.3. Executing Mining Operations
    11.1.4. Exploring Mining Capabilities
    11.1.5. Finding DME and JDM Version Information
    11.1.6. Object List Methods
    11.1.7. Model and Data Load Methods
    11.2. Using JDM Enumerations
    11.3. Using Data Specification Interfaces
    11.4. Using Classification Interfaces
    11.4.1. Classification Settings
    11.4.2. Algorithm Settings
    11.4.3. Model Contents
    11.4.4. Test Metrics for Model Evaluation
    11.4.5. Applying a Model to Data in Batch
    11.4.6. Applying a Model to a Single Record – Real-Time Scoring
    11.5. Using Regression Interfaces
    11.6. Using Attribute Importance Interfaces
    11.7. Using Association Interfaces
    11.8. Using Clustering Interfaces
    11.9. Summary
    11.10. References

  1. Chapter 10 – XML Schema
    12.1. Overview
    12.2. Schema Elements
    12.3. Schema Types
    12.4. Using PMML with the JDM Schema
    12.5. Use Cases for JDM XML Schema and Documents
    12.6. Summary
    12.7. References

  2. Chapter 11 – Web Services
    13.1. What is a Web Service?
    13.2. Service-Oriented Architecture
    13.3. JDM Web Service
    13.3.1. Overview of JDMWS Operations
    13.3.2. JDMWS Use Case
    13.3.3. JDM WSDL
    13.3.4. Data Exchange and Security in JDMWS
    13.4. Enabling JDM Web Services Using JAX-RPC
    13.4.1. Overview of JAX-RPC
    13.4.2. Build JDMWS Using JAX-RPC
    13.5. Summary
    13.6. References

Part III – Practice

  1. Chapter 12 – Practical Problem Solving
    14.1. Business Scenario 1: Targeted Marketing Campaign
    14.1.1. Campaign Specifications
    14.1.2. Design of the “Campaign Optimization” Object
    14.1.3. Code Examples
    14.1.4. Scenario I Conclusion
    14.2. Business Scenario 2: Understanding Key Factors
    14.2.1. Code Example
    14.2.2. Scenario 2 Conclusion
    14.3. Business Scenario 3: Using Customer Segmentation
    14.3.1. Customer Segmentation Specifications
    14.3.2. Design of the CustomerSegmenter Object
    14.3.3. Code Examples
    14.3.4. Scenario 3 Conclusion

  • 12.4 Summary

  • References

  • Chapter 13 – Building Data Mining Tools Using JDM
    17.1. Data Mining Tools
    17.1.1. Architecture of the Demonstration Interfaces
    17.1.2. Managing JDM Exceptions
    17.2. Administrative Console
    17.2.1. Creating the Connection
    17.2.2. Retrieving the List of Classes That Can Be Saved
    17.2.3. Retrieving the List of Saved Objects
    17.2.4. Rename a Saved Object
    17.2.5. Delete a Saved Object from the MOR
    17.3. User Interface to Build and Save a Model
    17.3.1. General Introduction
    17.3.2. Getting the Metadata
    17.3.3. Computing Statistics
    17.3.4. Retrieving the Statistics Information
    17.3.5. Saving the Physical Dataset, Build Settings, and Tasks
    17.4. User Interface to Test Model Quality
    17.4.1. Getting the List of Saved Models
    17.4.2. Computing the Test Metrics
    17.5. Summary

  • Chapter 14 – Getting Started with JDM Web Services
    18.1. A Web Service Client in PhP
    18.1.1. Filling the Input Values Using Javascript
    18.1.2. Saving the ApplySettings Object
    18.1.3. Retrieving the List of Models
    18.1.4. Executing RecordApply Task on Models
    18.2. A Web Service Client in Java
    18.2.1. How to Generate Java Classes with Axis
    18.2.2. Opening the Connection to a JDMWS Live Server
    18.2.3. Creating Build Settings
    18.2.4. Creating a Physical DataSet
    18.2.5. Creating a Build Task
    18.2.6. Executing a BuildTask

  • 14.3 Summary

  • References

  • Chapter 15 – Impacts on IT Infrastructure
    21.1. What Does Data Mining Require from IT?
    21.2. Impacts on Computing Hardware
    21.3. Impacts on Data Storage Hardware
    21.4. Data Access
    21.4.1. Data Access for Model Building
    21.4.2. Data Access for Apply and Test
    21.5. Backup and Recovery
    21.6. Scheduling
    21.7. Workflow
    21.8. Summary

  • References

  • Chapter 16 – Vendor Implementations
    23.1. Oracle Data Mining
    23.1.1. Oracle Position on JDM
    23.1.2. Oracle JDM Implementation Architecture
    23.1.3. Oracle JDM Capabilities
    23.1.4. Oracle JDM Extensions
    23.1.5. DME URI and Data URI
    23.1.6. Getting Started with OJDM
    23.1.7. Other Oracle Data Mining APIs
    23.1.8. Data Mining Graphical Interface Using OJDM
    23.2. ΚΧΕΝ (Knowledge Extraction Engines)
    23.2.1. KXEN Data Mining Activity
    23.2.2. KXEN Position on JDM
    23.2.3. KXEN JDM Implementation Architecture
    23.2.4. KXEN JDM Capabilities
    23.2.5. DME URI and Data URI Specifications
    23.2.6. KXEN Extensions
    23.2.7. KXEN Web Services Implementation
    23.3. Guidelines for New Implementers
    23.3.1. Standards Conformance
    23.3.2. Using the TCK
    23.4. Process for New JDM Users
    23.5. Summary

  • References

  1. Part IV – Wrapping Up

  2. Chapter 17 – Evolution of Data Mining Standards
    26.1. Data Mining Standards
    26.1.1. Predictive Model Markup Language
    26.1.2. Common Warehouse Metadata for Data Mining
    26.1.3. SQL/MM Part 6 Data Mining
    26.2. Java Community Process
    26.3. Why So Many Standards?
    26.4. Directions for Data Mining Standards
    26.5. Summary

  3. References

  4. Chapter 18 – Preview of Java Data Mining 2.0
    28.1. Transformations
    28.2. Time Series
    28.3. Apply for Association
    28.4. Feature Extraction
    28.5. Statistics
    28.6. Multi-target Models
    28.7. Text Mining
    28.8. Summary

  5. References

  6. Chapter 19 – Summary

  7. Further Reading

  8. Glossary

  9. Index

  10. About the Authors

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Tags: Mark Hornick, Sunil Venkayala, Erik Marcadé, Java Data, Strategy Standard, Guide for architecture