Mining Graph Data

Diane J. Cook, Lawrence B. Holder

  • 出版商: Wiley
  • 出版日期: 2006-11-01
  • 定價: $3,980
  • 售價: 8.5$3,383
  • 語言: 英文
  • 頁數: 500
  • 裝訂: Hardcover
  • ISBN: 0471731900
  • ISBN-13: 9780471731900
  • 相關分類: 大數據 Big-dataData Science
  • 立即出貨 (庫存=1)

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商品描述

Description

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.

There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

 

Table of Contents

Preface.

Acknowledgments.

Contributors.

1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook).

1.1 Terminology.

1.2 Graph Databases.

1.3 Book Overview.

References.

Part I GRAPHS.

2 GRAPH MATCHING—EXACT AND ERROR-TOLERANT METHODS AND THE AUTOMATIC LEARNING OF EDIT COSTS (Horst Bunke and Michel Neuhaus).

2.1 Introduction.

2.2 Definitions and Graph Matching Methods.

2.3 Learning Edit Costs.

2.4 Experimental Evaluation.

2.5 Discussion and Conclusions.

References.

3 GRAPH VISUALIZATION AND DATA MINING (Walter Didimo and Giuseppe Liotta).

3.1 Introduction.

3.2 Graph Drawing Techniques.

3.3 Examples of Visualization Systems.

3.4 Conclusions.

References.

4 GRAPH PATTERNS AND THE R-MAT GENERATOR (Deepayan Chakrabarti and Christos Faloutsos).

4.1 Introduction.

4.2 Background and Related Work.

4.3 NetMine and R-MAT.

4.4 Experiments.

4.5 Conclusions.

References.

Part II MINING TECHNIQUES.

5 DISCOVERY OF FREQUENT SUBSTRUCTURES (Xifeng Yan and Jiawei Han).

5.1 Introduction.

5.2 Preliminary Concepts.

5.3 Apriori-based Approach.

5.4 Pattern Growth Approach.

5.5 Variant Substructure Patterns.

5.6 Experiments and Performance Study.

5.7 Conclusions.

References.

6 FINDING TOPOLOGICAL FREQUENT PATTERNS FROM GRAPH DATASETS (Michihiro Kuramochi and George Karypis).

6.1 Introduction.

6.2 Background Definitions and Notation.

6.3 Frequent Pattern Discovery from Graph Datasets—Problem Definitions.

6.4 FSG for the Graph-Transaction Setting.

6.5 SIGRAM for the Single-Graph Setting.

6.6 GREW—Scalable Frequent Subgraph Discovery Algorithm.

6.7 Related Research.

6.8 Conclusions.

References.

7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar).

7.1 Introduction.

7.2 Mining Graph Data Using Subdue.

7.3 Comparison to Other Graph-Based Mining Algorithms.

7.4 Comparison to Frequent Substructure Mining Approaches.

7.5 Comparison to ILP Approaches.

7.6 Conclusions.

References.

8 GRAPH GRAMMAR LEARNING (Istvan Jonyer).

8.1 Introduction.

8.2 Related Work.

8.3 Graph Grammar Learning.

8.4 Empirical Evaluation.

8.5 Conclusion.

References.

9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio).

9.1 Introduction.

9.2 Graph-Based Induction Revisited.

9.3 Problem Caused by Chunking in B-GBI.

9.4 Chunkingless Graph-Based Induction (Cl-GBI).

9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI).

9.6 Conclusions.

References.

10 SOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING (Michel Liquière).

10.1 Presentation.

10.2 Basic Concepts and Notation.

10.3 Formal Concept Analysis.

10.4 Extension Lattice and Description Lattice Give Concept Lattice.

10.5 Graph Description and Galois Lattice.

10.6 Graph Mining and Formal Propositionalization.

10.7 Conclusion.

References.

11 KERNEL METHODS FOR GRAPHS (Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel).

11.1 Introduction.

11.2 Graph Classification.

11.3 Vertex Classification.

11.4 Conclusions and Future Work.

References.

12 KERNELS AS LINK ANALYSIS MEASURES (Masashi Shimbo and Takahiko Ito).

12.1 Introduction.

12.2 Preliminaries.

12.3 Kernel-based Unified Framework for Importance and Relatedness.

12.4 Laplacian Kernels as a Relatedness Measure.

12.5 Practical Issues.

12.6 Related Work.

12.7 Evaluation with Bibliographic Citation Data.

12.8 Summary.

References.

13 ENTITY RESOLUTION IN GRAPHS (Indrajit Bhattacharya and Lise Getoor).

13.1 Introduction.

13.2 Related Work.

13.3 Motivating Example for Graph-Based Entity Resolution.

13.4 Graph-Based Entity Resolution: Problem Formulation.

13.5 Similarity Measures for Entity Resolution.

13.6 Graph-Based Clustering for Entity Resolution.

13.7 Experimental Evaluation.

13.8 Conclusion.

References.

Part III APPLICATIONS.

14 MINING FROM CHEMICAL GRAPHS (Takashi Okada).

14.1 Introduction and Representation of Molecules.

14.2 Issues for Mining.

14.3 CASE: A Prototype Mining System in Chemistry.

14.4 Quantitative Estimation Using Graph Mining.

14.5 Extension of Linear Fragments to Graphs.

14.6 Combination of Conditions.

14.7 Concluding Remarks.

References.

15 UNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS (Mohammed Zaki).

15.1 Introduction.

15.2 Preliminaries.

15.3 Related Work.

15.4 Generating Candidate Subtrees.

15.5 Frequency Computation.

15.6 Counting Distinct Occurrences.

15.7 The SLEUTH Algorithm.

15.8 Experimental Results.

15.9 Tree Mining Applications in Bioinformatics.

15.10 Conclusions.

References.

16 DENSE SUBGRAPH EXTRACTION (Andrew Tomkins and Ravi Kumar).

16.1 Introduction.

16.2 Related Work.

16.3 Finding the densest subgraph.

16.4 Trawling.

16.5 Graph Shingling.

16.6 Connection Subgraphs.

16.7 Conclusions.

References.

17 SOCIAL NETWORK ANALYSIS (Sherry E. Marcus, Melanie Moy, and Thayne Coffman).

17.1 Introduction.

17.2 Social Network Analysis.

17.3 Group Detection.

17.4 Terrorist Modus Operandi Detection System.

17.5 Computational Experiments.

17.6 Conclusion.

References.

Index.

商品描述(中文翻譯)

描述

這本書專注且全面地探討以圖形表示的數據挖掘,提供最新的理論和實踐應用。即使您對分析圖形數據的背景知識很少,通過這本書,您將能夠將數據表示為圖形,從數據中提取模式和概念,並將書中介紹的方法應用於真實數據集。

本書附帶的網頁連結有一個錯誤。對於那些希望在本書中嘗試技術或在圖形數據上測試自己想法的讀者,本書的網頁應該是http://www.eecs.wsu.edu/MGD。

目錄

前言
致謝
貢獻者
第1章 簡介(Lawrence B. Holder和Diane J. Cook)
1.1術語
1.2圖形數據庫
1.3書籍概述
參考文獻
第一部分 圖形
第2章 圖形匹配-精確和容錯方法以及編輯成本的自動學習(Horst Bunke和Michel Neuhaus)
2.1介紹
2.2定義和圖形匹配方法
2.3學習編輯成本
2.4實驗評估
2.5討論和結論
參考文獻
第3章 圖形可視化和數據挖掘(Walter Didimo和Giuseppe Liotta)
3.1介紹
3.2圖形繪製技術
3.3可視化系統示例
3.4結論
參考文獻
第4章 圖形模式和R-MAT生成器(Deepayan Chakrabarti和Christos Faloutsos)
4.1介紹
4.2背景和相關工作
4.3NetMine和R-MAT
4.4實驗
4.5結論
參考文獻
第二部分 挖掘技術
第5章 發現頻繁子結構(Xifeng Yan和Jiawei Han)
5.1介紹
5.2初步概念
5.3基於Apriori的方法
5.4模式增長方法
5.5變體子結構模式
5.6實驗和性能研究
5.7結論
參考文獻
第6章 從圖形數據集中找到拓撲頻繁模式(Michihiro Kuramochi和George Karypis)
6.1介紹
6.2背景定義和符號
6.3從圖形數據集中發現頻繁模式-問題定義
6.4圖形交易的FSG