NDLI: A review on outlier detection techniques on data stream by using different approaches of K-Means algorithm

Content Provider	IEEE Xplore Digital Library
Author	Chauhan, P. Shukla, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., Marwadi Educ. Found.'s Group of Instn., Rajkot, India (Chauhan, P.; Shukla, M.)
Abstract	Data Stream mining has gained attraction from many researchers as there is need to mine large dataset which pose different challenges for researchers. Stream data is different compared to normal data as they are continuously produced from different applications which impose different challenges like massive, infinite, concept drift for processing. An object that does not obey the behavior of normal data object is called outliers. Outlier detection is used in different applications like fraud detection, intrusion detection, track environmental changes, medical diagnosis so there is need to detect outliers from data streams. Various approaches are used for outlier detection. Some of them use K-Means algorithm for outlier detection in data streams which help to create a similar group or cluster of data points. Data stream clustering techniques are highly helpful to cluster similar data items in data streams and also to detect the outliers from them, so they are called cluster based outlier detection. K-means algorithm is partition based algorithm which is used for clustering datasets into number of clusters. It is most common and popular algorithm for clustering due to its simplicity and efficiency. Purpose of this paper is to review of different approaches of outlier detection which is used for K-Means algorithm for clustering dataset with some other methods. Different application areas of outlier detection are discussed in this paper.
Starting Page	580
Ending Page	585
File Size	341993
Page Count	6
File Format	PDF
e-ISBN	9781467369114
DOI	10.1109/ICACEA.2015.7164758
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computers Data stream Application area of outlier detection Clustering algorithms K-Means algorithm Feature extraction Spatial databases Partitioning algorithms Data mining Outlier detection algorithm in data stream
Content Type	Text
Resource Type	Article

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