Outlier Detection by Using: R Software

Outlier Detection is a critical topic in Data Mining study. The practice of retrieving hidden and usable information from a large data set is known as Data Mining. Data Mining covers authentic, useful and high quality of knowledge. An Outlier is an observation that differs from the rest of dataset?s observation. Outlier detection from a collection of datasets is a well-known Data Mining process. Outliers help in detection of unusual patterns and behaviors of different data points which can give a useful result for the research. In data pre-processing and data mining, outlier detection is essential. Outliers have various applications area such as fraud detection, intrusion detection, medical and public health outlier detection, image detection, etc. This paper gives a brief description of outlier?s concept its types, causes and applications. Also provides a brief detail about the existing outlier detection algorithm; as to explain the richness and complexity associated with algorithms. Another part of the paper is focused on application of outlier detection algorithm by using R programming. This Software is open statistical source which is use for the analysis of data. In this paper some of the existing algorithms of outlier detection are implemented on our data set by using R Software. Data is taken from R software; the most fascinating part of R is that data is very easily accessible. The Outliers are considered as key in the discovery of unpredicted knowledge.