Advanced Methods for Knowledge Discovery from Complex Data

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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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.

• Bayesian network
• algorithms
• classification
• computer science
• data mining
• database
• evolution
• graph
• image databases
• kernel
• knowledge
• knowledge discovery
• learning
• machine learning
• ontology
• algorithm analysis and problem complexity

• 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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