Design and Implementation of Data Mining Tools

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A01=Bhavani Thuraisingham
A01=Latifur Khan
A01=Lei Wang
A01=Mamoun Awad
advanced intrusion detection techniques
anomaly detection algorithms
Association Rule Mining
Author_Bhavani Thuraisingham
Author_Latifur Khan
Author_Lei Wang
Author_Mamoun Awad
automatic
Automatic Image Annotation
Botnet Detection
Category=UMZ
Category=UN
Category=UNF
Category=UY
Category=UYQM
computer security analytics
Data Mining
Data Mining Techniques
Data Mining Tool
Dempster's Rule
Dempster’s Rule
detection
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Image Annotation
Image Classification
intrusion
Intrusion Detection
KNN Classifier
machine
Malicious Code Detection
Markov Model
Mine Multimedia Data
MIT Lincoln Lab
Multimedia Data
multimedia information retrieval
neural network modeling
page
pattern recognition systems
prediction
predictive analytics web usage
RBF Kernel
Subspace Clustering
Subspace Clustering Algorithm
support
surfing
SVM
SVM Training
vector
web
Web Data Management
Web Data Mining
Web Usage Mining
Www Prediction

Product details

  • ISBN 9781420045901
  • Weight: 600g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Jun 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject.

The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification.

Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.

Thuraisingham, Bhavani; Khan, Latifur; Awad, Mamoun; Wang, Lei

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