Enhanced Classification via Clustering Techniques using Decision Tree for Feature Selection

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Foundation of Computer Science (FCS), NY, USA


Information overload has raggedly increased as a result of the advances in the aspect of storage capabilities and data collection in previous years. The growth seen in the number of observation has partly cause a collapse in analytical method but the increases in the number of variable associated with each observation has grossly collapse it. The number of variables that are measured on each observation.is referred to as the dimension of the data, and a major problem of dataset containing high dimensions is that, there exist only few “important” measured variables for understanding the fundamental occurrences of interest. Hence, dimension reduction of the original data prior to any modeling of the data is of great necessity today. In this paper, a précis of K-Means, Expectation Maximization and J48 decision tree classifier is presented with a framework on the performance measurement of base classifiers with and without feature reduction. A performance evaluation was carried out based on F-Measure, Precision, Recall, True Positive Rate, False Positive Rate, ROC Area and Time taken to build model. The experiment revealed that the reduced dataset yielded improved results than the full dataset after performing classification via clustering.



Intrusion Detection, Machine Learning, Data Mining, Network Security


Balogun Abdullateef O., Mabayoje Modinat A., Salihu Shakirat and Arinze Salvation A.. Article: Enhanced Classification via Clustering Techniques using Decision Tree for Feature Selection. International Journal of Applied Information Systems 9(6):11-16, September 2015.