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  1. Data Mining and User Profiling for an E-Commerce System

    Many companies are now develo** an online internet presence to sell or promote their products and services. The data generated by e-commerce sites...
    Ken McGarry, Andrew Martin, Dale Addison in Classification and Clustering for Knowledge Discovery
    Chapter

  2. Discovery of Fuzzy Multiple-Level Web Browsing Patterns

    Web usage mining is the application of data mining techniques to discover usage patterns from web data. It can be used to better understand web usage...
    Shyue-Liang Wang, Wei-Shuo Lo, Tzung-Pei Hong in Classification and Clustering for Knowledge Discovery
    Chapter

  3. Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings

    The aim of this work is to compare the performances of 5 classifiers (linear and quadratic classifiers, k nearest neighbors, Parzen kernels and...
    Guillaume Becq, Sylvie Charbonnier, ... Pierre Baconnier in Classification and Clustering for Knowledge Discovery
    Chapter

  4. A Probabilistic Approach to Mining Fuzzy Frequent Patterns

    Deriving association rules is a typical task in data mining. The problem was originally defined for transactions of discrete items, but it was soon...
    Attila Gyenesei, Jukka Teuhola in Classification and Clustering for Knowledge Discovery
    Chapter

  5. 4. Stability of Nonlinear Switched and Impulsive Systems

    In this chapter, we shall study the stability of nonlinear switched and impulsive systems of the form...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter

  6. Measuring User Satisfaction in Web Searching

    Search engines are among the most popular as well as useful services on the web. But the problem we face is due to the large number of search engines...
    M. M. Sufyan Beg, Nesar Ahmad in Computational Intelligence for Modelling and Prediction
    Chapter

  7. A Fuzzy Method for Learning Simple Boolean Formulas from Examples

    We discuss a method for inferring Boolean functions from examples. The method is inherently fuzzy in two respects: i) we work with a pair of formulas...
    Bruno Apolloni, Andrea Brega, ... Anna Maria Zanaboni in Computational Intelligence for Modelling and Prediction
    Chapter

  8. Kernel Discriminant Learning with Application to Face Recognition

    When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer...
    J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos in Support Vector Machines: Theory and Applications
    Chapter

  9. Multiple Model Estimation for Nonlinear Classification

    This chapter describes a new method for nonlinear classification using a collection of several simple (linear) classifiers. The approach is based on...
    Y. Ma, V. Cherkassky in Support Vector Machines: Theory and Applications
    Chapter

  10. Active Support Vector Learning with Statistical Queries

    The article describes an active learning strategy to solve the large quadratic programming (QP) problem of support vector machine (SVM) design in...
    P. Mitra, C.A. Murthy, S.K. Pal in Support Vector Machines: Theory and Applications
    Chapter

  11. Sequential Pattern Mining*

    Sequential pattern discovery has emerged as an important research topic in knowledge discovery and data mining with broad applications. Previous...
    Tian-Rui Li, Yang Xu, ... Wu-ming Pan in Intelligent Data Mining
    Chapter

  12. Uncertain Knowledge Association Through Information Gain

    The problem of entity association is at the core of information mining techniques. In this work we propose an approach that links the similarity of...
    Athena Tocatlidou, Da Ruan, ... Nikos A. Lorentzos in Intelligent Data Mining
    Chapter

  13. Soft Computing Paradigms for Web Access Pattern Analysis

    Web servers play a crucial role to convey knowledge and information to the end users. With the popularity of the WWW, discovering the hidden...
    **aozhe Wang, Ajith Abraham, Kate A. Smith in Classification and Clustering for Knowledge Discovery
    Chapter

  14. Ontology-based Fuzzy Decision Agent and Its Application to Meeting Scheduling Support System

    A Fuzzy Decision Agent (FDA) based on personal ontology for Meeting Scheduling Support System (MSSS) is proposed in this chapter. In this system,...
    Chang-Shing Lee, Hei-Chia Wang, Meng-Ju Chang in Classification and Clustering for Knowledge Discovery
    Chapter

  15. D-GridMST: Clustering Large Distributed Spatial Databases

    In this paper, we will propose a novel distributable clustering algorithm, called Distributed-GridMST (D–GridMST for short), which deals with large...
    Ji Zhang, Han Liu in Classification and Clustering for Knowledge Discovery
    Chapter

  16. Data Mining of Missing Persons Data

    This paper presents the results of analysis to evaluate the effectiveness of data mining techniques to predict the outcome for missing persons cases....
    K. Blackmore, T. Bossomaier, ... D. Thomson in Classification and Clustering for Knowledge Discovery
    Chapter

  17. 1. Examples and Modelling of Switched and Impulsive Systems

    When you begin to read this book, you may ask: “what is a switched and impulsive system?” This is a question that may be best answered through...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter

  18. Fast Color Texture-Based Object Detection in Images: Application to License Plate Localization

    The current chapter presents a color texture-based method for object detection in images. A support vector machine (SVM) is used to classify each...
    K.I. Kim, K. Jung, H.J. Kim in Support Vector Machines: Theory and Applications
    Chapter

  19. Gas Sensing Using Support Vector Machines

    In this chapter we deal with the use of Support Vector Machines in gas sensing. After a brief introduction to the inner workings of multisensor...
    J. Brezmes, E. Llobet, ... J.W. Gardner in Support Vector Machines: Theory and Applications
    Chapter

  20. Support Vector Machines – An Introduction

    This is a book about learning from empirical data (i.e., examples, samples, measurements, records, patterns or observations) by applying support...
    V. Kecman in Support Vector Machines: Theory and Applications
    Chapter
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