UID:
almahu_9948025835802882
Umfang:
1 online resource (545 p.)
Ausgabe:
1st edition
ISBN:
1-281-01198-3
,
9786611011987
,
0-08-049591-5
Serie:
The Morgan Kaufmann series in data management systems
Inhalt:
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-a
Anmerkung:
Description based upon print version of record.
,
Front Cover; Java Data Mining: Strategy, Standard, and Practice; Copyright Page; Contents; Preface; Guide to Readers; Part I : Strategy; Chapter 1. Overview of Data Mining; 1.1 Why Data Mining Is Relevant Today?; 1.2 Introducing Data Mining; 1.3 The Value of Data Mining; 1.4 Summary; References; Chapter 2. Solving Problems in Industry; 2.1 Cross-Industry Data Mining Solutions; 2.2 Data Mining in Industries; 2.3 Summary; References; Chapter 3. Data Mining Process; 3.1 A Standardized Data Mining Process; 3.2 A More Detailed View of Data Analysis and Preparation
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3.3 Data Mining Modeling, Analysis, and Scoring Processes3.4 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; References; Chapter 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; References; Chapter 5. JDM Strategy; 5.1 What Is the JDM Strategy?; 5.2 Role of Standards; 5.3 Summary; References; Chapter 6. Getting Started; 6.1 Business Understanding
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6.2 Data Understanding6.3 Data Preparation; 6.4 Modeling; 6.5 Evaluation; 6.6 Deployment; 6.7 Summary; References; Part II : Standards; Chapter 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; References; Chapter 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 Uniform Resource Identifiers for Datasets; 8.6 Enumerated Types; 8.7 Exceptions
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8.8 Discovering DME Capabilities8.9 Summary; References; Chapter 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; References; Chapter 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; References
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Chapter 11. Web Services11.1 What is a Web Service?; 11.2 Service-Oriented Architecture; 11.3 JDM Web Service; 11.4 Enabling JDM Web Services Using JAX-RPC; 11.5 Summary; References; Part III : Practice; Chapter 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; References; Chapter 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
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13.4 User Interface to Test Model Quality
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English
Weitere Ausg.:
ISBN 0-12-370452-9
Sprache:
Englisch
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