Top Ten Algorithms in Data Mining

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academic research methods
advanced data mining techniques
algorithms
artificial intelligence applications
Association Rules
Base Learning Algorithm
Bayes Classifier
Bayes Error
Bayes Method
Bayes Model
Big Data
Candidate Itemsets
Cart Algorithm
Cart Model
Cart Tree
Category=UMB
Category=UNF
Cluster Id
Cluster Representatives
computational statistics
Cost Sensitive Learning
data analytics
data mining
Data Sets
decision
deep learning
ECM Algorithm
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Frequent Itemsets
Generalization Error
Ionosphere Data Set
k-means
learning
link mining
machine
machine learning
Naive Bayes
Optimal Hyperplane
Petal Length
Power Iteration Method
predictive modelling
repository
Sequential Pattern Mining
set
statistical learning
supervised classification
support
Terminal Nodes
training
tree
uci
UCI Machine Learn Repository
UCI Repository
unsupervised clustering
vector
Vipin Kumar
Xindong Qu

Product details

  • ISBN 9781420089646
  • Weight: 590g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Apr 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

University of Vermont, Burlington, USA University of Minnesota, Minneapolis, USA