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Data Classification

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algorithms
Bayesian Model Averaging
Category=UN
Category=UYQM
Class Association Rules
Classification Algorithms
Concept Drift
Conditional Probability Distribution
Data Set
decision
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
feature selection methods
label
machine
machine learning techniques
Naive Bayes Classifier
Profile HMM
Rare Class
rare class detection in scientific datasets
RBFN
Roc Curve
rule-based systems
semi-supervised learning
Semi-supervised Learning Algorithms
Semisupervised Learning
Sparse Group Lasso
support
support vector machines
SVM Approach
SVM Classification
SVM Classifier
SVM Method
test
Test Instance
time series analysis
Time Series Classification
training
trees
unlabeled
Unlabeled Data
Unlabeled Examples
vector

Product details

  • ISBN 9781466586741
  • Weight: 1451g
  • Dimensions: 178 x 254mm
  • Publication Date: 25 Jul 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data.

This comprehensive book focuses on three primary aspects of data classification:

  • Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks.
  • Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm.
  • Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
About

Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.