(untitled) (International Journal of Engineering and Computer Science, 2015)
Outlier detection and is an important branch of data mining. Data mining is extensively studied field of research area; where most of the work is focused on the information discovery. A data stream is a massive sequence of data objects continuously generated at much faster rate. There are various approaches and methods 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. The K-means algorithm is the best known partitioned clustering algorithm. As we know that streaming data often fails to scan the multiple items and also the new concepts may keep evolving in coming data over time hence the outlier detection plays the challenging role in the streaming data. The irrelevant attributes can be termed as noisy attribute at the time of working with the data streams objects and such attributes imposes the challenge. In high dimensional data the number of attributes associated with the dataset is very large and it makes the dataset unmanageable. Clustering is a data stream mining task which is very useful to gain insight of data and data characteristics. Clustering is also used as a pre-processing step in over all mining process for an example clustering is used for outlier detection and for building and development of Hybrid approach. Purpose of this paper is to review of Hybrid approach of outlier detection with others approach which uses K-Means algorithm for clustering dataset with some other techniques like Euclidean distance approach. Various application domains of outlier detection are discussed in this paper.