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Classification Algorithms of Data Mining
Objectives: To make a comparative study about different classification techniques of data mining. Methods: In this paper some data mining techniques like Decision tree algorithm, Bayesian network model, Naive Bayes method, Support Vector Machine and K-Nearest neighbour classifier were discussed. Findings: Each algorithm has its own advantages and disadvantages. Decision tree technique do not perform well if the data have smooth boundaries. The Naive Bayesian classifier works with both continuous and discrete attributes and works well for real time problems. This method is very fast and highly scalable. The drawback of this technique is when a data set which has strong dependency among the attribute is considered then this method gives poor performance. KNN can perform well in many situations and it particularly suits well for multi-model classes as well as applications in which an object can have many labels. The drawback of KNN is it involves lot of computation and when the size of training set taken is large then the process will become slow. Support vector machine suites well when the data need to be classified into two groups. Application: This paper is to provide a wide range of idea about different classification algorithms..
Bayesian Network, Data Mining, Decision Tree, K-Nearest Neighbour Classifier, Naive Bayes, Support Vector Machine.
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