Classification Of Using Data Mining Technique Essay

1508 Words Jan 26th, 2016 7 Pages
Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier. Types of classification models:
• Classification by decision tree induction
• Bayesian Classification
• Neural Networks
• Support Vector Machines (SVM)
• Classification Based on Associations
Decision tree
Decision trees are known as effective classifiers in a variety of domains. Logistic regression and decision-tree induction have different underlying assumptions. For logistic regression, it is assumed that the influence of a variable on the outcome is uniform across all subjects unless specific interactions with other variables are included. However, the decision tree…

Related Documents