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Advanced Methods for Knowledge Discovery from Complex Data

  • Book
  • © 2005

Overview

Authors:
  1. Sanghamitra Bandyopadhyay

    1. Indian Statistical Institute, Machine Intelligence Unit, Kolkata, India

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  2. Ujjwal Maulik

    1. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

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  3. Lawrence B. Holder

    1. University of Texas at Arlington, Department of Computer Science & Engineering, USA

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  4. Diane J. Cook

    1. University of Texas at Arlington, Department of Computer Science & Engineering, USA

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  • Covers a variety of advanced data mining techniques
  • Does not limit discussion to one specific domain area
  • First book to focus on advances on the synergy between application domains and algorithm types rather than limit the scope to a particular domain / type
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Foundations

    1. Knowledge Discovery and Data Mining

      • Sanghamitra Bandyopadhyay, Ujjwal Maulik
      Pages 3-42

    2. Automatic Discovery of Class Hierarchies via Output Space Decomposition

      • Joydeep Ghosh, Shailesh Kumar, Melba M. Crawford
      Pages 43-73

    3. Graph-based Mining of Complex Data

      • Diane J. Cook, Lawrence B. Holder, Jeff Coble, Joseph Potts
      Pages 75-93

    4. Predictive Graph Mining with Kernel Methods

      • Thomas Gärtner
      Pages 95-121

    5. TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees

      • Mohammed J. Zaki
      Pages 123-151

    6. Sequence Data Mining

      • Sunita Sarawagi
      Pages 153-187

    7. Link-based Classification

      • Lise Getoor
      Pages 189-207

  3. Applications

    1. Knowledge Discovery from Evolutionary Trees

      • Sen Zhang, Jason T. L. Wang
      Pages 211-230

    2. Ontology-Assisted Mining of RDF Documents

      • Tao Jiang, Ah-Hwee Tan
      Pages 231-252

    3. Image Retrieval using Visual Features and Relevance Feedback

      • Sanjoy Kumar Saha, Amit Kumar Das, Bhabatosh Chanda
      Pages 253-283

    4. Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection

      • Srinivas Mukkamala, Andrew H. Sung
      Pages 285-306

    5. On-board Mining of Data Streams in Sensor Networks

      • Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady Zaslavsky
      Pages 307-335

    6. Discovering an Evolutionary Classifier over a High-speed Nonstatic Stream

      • Jiong Yang, Xifeng Yan, Jiawei Han, Wei Wang
      Pages 337-363

  4. Back Matter

    Pages 365-369
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Keywords

About this book

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.

Authors and Affiliations

  • Indian Statistical Institute, Machine Intelligence Unit, Kolkata, India

    Sanghamitra Bandyopadhyay

  • Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

    Ujjwal Maulik

  • University of Texas at Arlington, Department of Computer Science & Engineering, USA

    Lawrence B. Holder, Diane J. Cook

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