期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2016
卷号：5
期号：1
页码：15453-15456
DOI：10.18535/Ijecs/v5i1.9
出版社：IJECS
摘要：Data mining is a highly researched area in the today’s world as data is crucial part of many application, dueto which many researchers express their interest in this domain. As there arises a need to process largedataset which imposes different challenges for researchers. To have a data which is free from a noisyattributes , known as a filtered data , is of much important to gain accuracy in a result sets. For that , findingand eliminate the noisy objects has gained a much more importance. An object that does not follow thefootprints of usual data object is called outliers. Outlier detection process is used in numerous applicationslike fraud detection, intrusion detection system, tracking environmental activities, healthcare diagnosis.Numbers of approaches are used in the process of detection of outlier. Most approaches focuses to useCluster-based and Distance based approach (i.e. using K- Means algorithm and Euclidian distance) foroutlier detection in data sets which help them to create a group of similar elements or cluster of data points.Clustering techniques are highly useful for grouping similar data items from data sets and after that byapplying distance based calculations, detection of outlier is done, so they are called cluster-based outlierdetection. K- Means and Euclidian distance are the most common and popular algorithm for clustering andoutlier detection process due to its simplicity and efficiency. Different application areas of outlier detectionare discussed in this paper.
关键词：Cluster-based; Dataset; Distance-based; Dynamic data Stream; K-Means; Outlier Detection.
