Data Mining Learning Models and Algorithms on a Scada System Data Repository

Abstract

This paper presents three data mining techniques applied
on a SCADA system data repository: Naijve Bayes, k-Nearest Neighbor and Decision Trees. A conclusion that k-Nearest Neighbor is a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation. The experiments are built on the training data set and evaluated using the new test set with machine learning tool WEKA.