When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspicion by differing significantly from the majority of data is called anomaly detection. With progress in technology and the widespread use of data for the purpose of business, spam faced by individuals and companies is increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, machine learning, artificial intelligence, deep learning, image processing, cloud computing, audio processing, video processing, VoIP, data science, wireless sensor, etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data. This book will mainly target researchers and higher graduate learners in computer science and data science.