A STUDY ON DIFFERENT APPROACHES OF OUTLIER DETECTION IN DATA MINING

Data mining is a process of extracting knowledge from large databases. Knowledge is appreciated as ultimate power now a days and considered as very important factor for the success of any organization because it has impacted the role of people working in that organization. Outlier detection is an important task in data mining and it has got many real time applications. The majority of real-time data contains certain unwanted or unrelated values, generally termed as “outliers”. The segregation of outlier improves the quality of data, and thereby the accuracy rate is increased. The outlier is either individual or groups that depend upon the data and applications. Outlier occurs due to various reasons such as automatic faults, behavioral changes in the system, human error, irrelevant data and instrument faults. This paper presents an overview of outlier concepts, taxonomy, approaches and review of outlier detection algorithms and techniques.

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