In the present paper a 2-means clustering-based anomaly detection technique is proposed. The presented method parses the set of training data, consisting of normal and anomaly data, and separates the data into two clusters. Each cluster is represented by its centroid one of the normal observations, and the other for the anomalies. The paper also provides appropriate methods for clustering, trai...