• Classification rules,
extracted from decision trees,
IF-THEN expressions and all the tests have
to succeed if each rule is to be generated.
• Separates the complex problem into many
• resolves the sub problems through
K-Nearest Neighbor Algorithm
• Locate the nearest neighbors.
• Neighbors used to classify the new sample.
• Easy detect.
• It is unsupervised learning.
Support vector machine
• Kernel representation.
• Margin optimization.
• Detect the problem.
• Best supportive technique
• Trees constructed.
• Favour for SVM.
❖Support vector machine.
❖This two are the important techniques in
data mining which is together
called logistic regression
• Logistic Regression can minimize the fraud rate.
• It is easy to implement.
• C. Chen, A. Liaw, L. Breiman, Using Random Forest to Learn
Imbalanced Data,Technical Report 666, University of California at
Berkeley, Statistics Department.
• A. Srivastava, A. Kundu, S. Sural, A.Majumdar, Credit card fraud
detection using hidden Markov model, IEEE Transactions on
Dependable and Secure Computing.