An anomaly is a deviation from the normal. In a large dataset or continuously streaming data an anomaly occurs when such data has occurred which either do not follow certain set of rules or pattern that every other data point follows. A key goal of information analytics is to identify patterns of anomalous behaviour. Such identification of anomalies is required in a variety of applications such as systems management, sensor networks, and security. However, most of the current state of the art on anomaly detection relies on using a predefined knowledge base. A key challenge is defining and creating the predefined knowledge base and the need to have prior information about the domain. There are several approaches for change or anomaly detection. In this paper we focus on the broad categories: the rule-based, pattern-matching and statistical approaches for anomaly detection.