Abstract
Discovery of a predictive learning function that classifies data item into one of several predefined classes. 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. 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 algorithm then encodes these parameters into a model called a classifier, Here Discussed classification algorithms Bayesian Classification, Support Vector Machines (SVM), K-Nearest Neighbor Classifies, Genetic Algorithm, Artificial Neural Networks, Decision Tree, Naïve Bayes,ID3,C4.5.
Keywords
Datamining
Prediction
K-Nearest Neighbor Classifies
Genetic Algorithm
Artificial Neural Networks
Decision Tree
Naïve Bayes
ID3
C4.5.
Authors
How to Cite this Article
M.Shanmugapriya, K.Sudha (2017).
"A STUDY ON PREDICTIVE DATA MINING ALGORITHMS".
International Journal of Contemporary Research in Computer Science and Technology,
3(11), pp. 29-35.
