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Unsupervised learning, also known as unsupervised machine learning, analyzes and clusters unlabeled data utlizing machine learning techniques. Without human input, these algorithms discover patterns or groupings in the data. In the domain of abuse and network intrusion detection, interesting objects are often short bursts of activity rather than rare objects. Anomaly detection is a difficult task that requires familiarity and a good understanding of the data and the pattern does not correspond to the common statistical definition of an outlier as an odd item. The traditional algorithms need data preparations while unsupervised algorithms can be prepared so that they can handle the data in war format. Anomaly detection, sometimes referred to as outlier analysis is a data mining procedure that detects events, data points, and observations that deviates from the expected behaviour of a dataset. The unsupervised …