Concept Data Analysis : Theory and Applications

Claudio Carpineto, Giovanni Romano

  • 出版商: Wiley
  • 出版日期: 2004-09-03
  • 定價: $4,200
  • 售價: 8.5$3,570
  • 語言: 英文
  • 頁數: 220
  • 裝訂: Hardcover
  • ISBN: 0470850558
  • ISBN-13: 9780470850558
  • 相關分類: Data Science
  • 立即出貨 (庫存 < 3)

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商品描述

Description:

The advent of the Web, along with the unprecedented amount of data available in electronic format, has dramatically increased the need for tools that support the users in retrieving, understanding and mining the information and knowledge contained in such data.

Concept data analysis differs from statistical data analysis in that the emphasis is on recognising and generalising the structure of symbolic data through a mathematical representation termed a concept lattice. Thanks to its simplicity, elegance and versatility, concept data analysis can effectively support various kinds of content management tasks using different or heterogeneous types of data.

  • Provides a comprehensive treatment of the full range of techniques developed for concept data analysis covering creation, maintenance, display and manipulation of concept lattices
  • Presents application areas such as information retrieval and mining from text and web data as well as rule mining from structured data
  • Features two detailed case studies; exploring the content of the ACM Digital Library using an interface that integrates multiple search functionalities; and mining web retrieval results through the system CREDO, a version of which is available on-line for testing

Concept Data Analysis: Theory & Applications is essential for researchers active in information processing and data mining as well as industry practitioners who are interested in creating a commercial product for concept data analysis or developing content management applications. Computer science students will also find it invaluable.

 

Table of Contents:

Foreword.

Preface.

I Theory and algorithms.

1 Theoretical foundations.

1.1 Basic notions of orders and lattices.

1.2 Context, concept, and concept lattice.

1.3 Many-valued contexts.

1.4 Bibliographic notes.

2 Algorithms.

2.1 Constructing concept lattices.

2.2 Incremental lattice update.

2.3 Visualization.

2.4 Adding knowledge to concept lattices.

2.5 Bibliographic notes.

II Applications.

3 Information retrieval.

3.1 Query modi.cation.

3.2 Document ranking

4 Text mining.

4.1 Mining the content of the ACM Digital Library.

4.2 MiningWeb retrieval results with CREDO.

4.3 Bibliographic notes.

5 Rule mining.

5.1 Implications.

5.2 Functional dependencies.

5.3 Association rules.

5.4 Classification rules.

5.5 Bibliographic notes.

商品描述(中文翻譯)

描述:
隨著網絡的出現,以及以電子格式提供的前所未有的大量數據,對於支持用戶檢索、理解和挖掘這些數據中包含的信息和知識的工具的需求急劇增加。概念數據分析與統計數據分析的不同之處在於,它強調通過一種稱為概念格的數學表示來識別和概括符號數據的結構。由於其簡單性、優雅性和多功能性,概念數據分析可以有效地支持使用不同或異構類型的數據進行各種內容管理任務。

主要特點:
- 提供了對概念數據分析的全面介紹,包括概念格的創建、維護、顯示和操作。
- 提供了應用領域,如從文本和網絡數據中檢索和挖掘信息,以及從結構化數據中挖掘規則。
- 提供了兩個詳細的案例研究:使用集成多種搜索功能的界面探索ACM數字圖書館的內容,以及通過CREDO系統挖掘網絡檢索結果,其中的一個版本可在線進行測試。

《概念數據分析:理論與應用》對於從事信息處理和數據挖掘的研究人員以及對於創建概念數據分析的商業產品或開發內容管理應用程序感興趣的行業從業人員來說是必不可少的。計算機科學學生也會發現它非常有價值。

目錄:
- 前言
- 前言
- 第一部分:理論和算法
- 第1章:理論基礎
- 1.1:順序和格的基本概念
- 1.2:上下文、概念和概念格
- 1.3:多值上下文
- 1.4:參考文獻
- 第2章:算法
- 2.1:構建概念格
- 2.2:增量格更新
- 2.3:可視化
- 2.4:向概念格添加知識
- 2.5:參考文獻
- 第二部分:應用
- 第3章:信息檢索
- 3.1:查詢修改
- 3.2:文檔排序
- 第4章:文本挖掘
- 4.1:挖掘ACM數字圖書館的內容