Physical Sciences and Mathematics Commons^™

K-Means+Id3 And Dependence Tree Methods For Supervised Anomaly Detection, Kiran S. Balagani

Doctoral Dissertations

In this dissertation, we present two novel methods for supervised anomaly detection. The first method "K-Means+ID3" performs supervised anomaly detection by partitioning the training data instances into k clusters using Euclidean distance similarity. Then, on each cluster representing a density region of normal or anomaly instances, an ID3 decision tree is built. The ID3 decision tree on each cluster refines the decision boundaries by learning the subgroups within a cluster. To obtain a final decision on detection, the k-Means and ID3 decision trees are combined using two rules: (1) the nearest neighbor rule; and (2) the nearest consensus rule. The …