EAS Publisher | Comparative Analysis of Various Data Mining Classification Algorithms

Comparative Analysis of Various Data Mining Classification Algorithms

DOI : https://doi.org/10.36349/easjecs.2019.v02i12.003

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Data mining is the process of digging through and analyzing various sets of data and then extracting the meaning of the data. Classification is a data mining method used to predict the class of objects whose class label is not known. There are many classification mechanisms used in data mining such as KNearest Neighbor (KNN), Bayesian network, Cross validated parameter selection (CVPS), Naive Bayes Multinominal Updatae- ble (NBMU) Algorithm, Fuzzy logic, Support vector machines etc. This paper presents a comparison on four classification techniques which are K-Nearest Neighbor, User Classifier, Cross validated parameter selection and Naive Bayes Multinominal Updataeble Algorithms. The goal of this research is to enumerate the best technique from above four analyzed under a given data set and provide a fruitful comparison result which can be used for further analysis or future development.

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