Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation

Mark F. Hornick, Erik Marcadé, Sunil Venkayala

  • 出版商: Morgan Kaufmann
  • 出版日期: 2006-11-01
  • 售價: $2,240
  • 貴賓價: 9.5$2,128
  • 語言: 英文
  • 頁數: 544
  • 裝訂: Paperback
  • ISBN: 0123704529
  • ISBN-13: 9780123704528
  • 相關分類: Java 程式語言Data-mining
  • 立即出貨 (庫存=1)

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商品描述

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 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

    商品描述(中文翻譯)

    描述

    無論您是軟體開發人員、系統架構師、資料分析師還是業務分析師,如果您想在開發先進的分析應用程式中利用資料探勘,Java Data Mining(JDM)是一個關鍵的解決方案組件。JDM是一個新的標準,現在已經在核心資料庫管理系統和資料探勘/分析軟體中實施。本書是使用JDM標準介面的使用指南,由JDM標準的貢獻者撰寫。

    本書討論並說明如何使用JDM API解決實際問題。作者為您提供:

    - 資料探勘介紹-資料探勘的概述以及它可以解決的跨行業問題;JDM在解決與資料探勘相關的問題的戰略解決方案中的地位;
    - JDM基礎知識-概念、設計方法和設計問題,並提供Java中詳細的程式碼示例;在SOA環境中啟用JDM功能的Web服務介面;以及JDM物件的JDM XML Schema的示例;
    - 實踐中的JDM-使用JDM的供應商實現和客戶應用程式的方法、整合和使用;資料探勘對IT基礎設施的影響;使用JDM API構建應用程式的操作指南。
    - 可在書中引用的可免費下載的KJDM原始碼,請點擊此處。

    目錄

    前言
    讀者指南

    第一部分-策略
    1. 資料探勘概述
    1.1. 為什麼資料探勘在今天如此重要?
    1.2. 介紹資料探勘
    1.3. 資料探勘的價值
    1.4. 摘要
    1.5. 參考文獻

    2. 在產業中解決問題
    2.1. 跨行業資料探勘解決方案
    2.2. 各行業中的資料探勘
    2.3. 摘要
    2.4. 參考文獻

    3. 資料探勘流程
    3.1. 標準化的資料探勘流程
    3.2. 資料分析和準備...更詳細的觀點
    3.3. 資料探勘建模、分析和評分流程
    3.4. 資料庫和資料倉庫在資料探勘中的角色
    3.5. 企業軟體架構中的資料探勘
    3.6. 自動化資料探勘的進展
    3.7. 摘要
    3.8. 參考文獻

    4. 探勘函數和演算法
    4.1. 資料探勘函數
    4.2. 分類
    4.3. 迴歸
    4.4. 屬性重要性
    4.5. 關聯
    4.6. 分群
    4.7. 摘要
    4.8. 參考文獻

    5. JDM策略
    5.1. JDM策略是什麼?
    5.2. 標準的角色
    5.3. 摘要
    5.4. 參考文獻

    6. 入門
    6.1. 商業理解
    6.2. 資料理解
    6.3. 資料準備
    6.4. 建模
    6.5. 評估
    6.6. 部署
    6.7. 摘要
    6.8. 參考文獻

    第二部分-標準
    7. Java資料探勘概念
    7.1. 分類問題
    7.2. 迴歸問題
    7.3. 屬性重要性
    7.4. 關聯規則問題
    7.5. 分群問題
    7.6. 摘要
    7.7. 參考文獻

    8. JDM API的設計
    8.1. 資料探勘概念的物件建模
    8.2. 模組化套件
    8.3. 連接架構
    8.4. 物件工廠
    8.5. 資料集的URI
    8.6. 列舉型別
    8.7. 例外
    8.8. Di