Hierarchical and Partitioning Clustering Algorithms for Detecting Outliers in Data Streams

Abstract

The data stream is a new arrival of research area in data mining where as data stream refers to the process of extracting knowledge structures from unlimited and fast growing data records. Future applications involved in data streams are motivated by many researchers involving continuous and massive data sets such as telecommunication system, customer click streams, ecommerce, meteorological data, network monitor, stock market and wireless sensor network. For handling this type of stream data, the recent data mining methods are not sufficient and equipped to deal with them, for this reason it leads to a numerous computational and mining challenges due to shortage of hardware limitations. Nowadays many researchers have focused on mining data streams and they proposed many techniques for data stream classification and clustering, as well as mining frequent items from data streams. Data stream clustering and outlier detection provides a number of unique challenges in evolving data stream environment. Data stream clustering algorithms are highly used for detecting the outliers in efficient manner. The main purpose of this research work is to perform the clustering process and detecting the outliers in data streams. In this research work, two types of clustering algorithms namely BIRCH with K-Means and CURE with K-means is used for finding the outliers in data streams. Two performance factors such as clustering accuracy and outlier detection accuracy are used for observation. Through examining the experimental results, it is observed that the CURE with K-Means clustering algorithm performance is more accurate than the BIRCH with K-Means algorithm.