A hybrid clustering algorithm for outlier detection in data streams

Abstract

In current years, data streams have been gradually turn into most important research area in the field of computer science. Data streams are defined as fast, limitless, unbounded, river flow, continuous, stop less, massive, tremendous unremitting, immediate, stream flow, arrival of ordered and unordered data. Data streams are divided into two types, they are online and offline streams. Online data streams are mainly used for real world applications like face book, twitter, network traffic monitoring, intrusion detection and credit card processes. Offline data streams are mainly used for manipulating the information which is based on web log streams. In data streams, data size is extremely huge and potentially infinite and it is not possible to lay up all the data, so it leads to a mining challenge where shortage of limitations has occur in hardware and software. Data mining techniques such as clustering, load shedding, classification and frequent pattern mining are to be applied in data streams to get useful knowledge. But, the existing algorithms are not suitable for performing the data mining process in data streams; hence there is a need for new techniques and algorithms. The main objective of this research work is to perform the clustering process in data streams and detecting the outliers in data streams. New hybrid approach is proposed which combines the hierarchical clustering algorithm and partitioning clustering algorithm. In hierarchical clustering, CURE algorithm is used and enhanced (E-CURE) and in partitioning clustering, CLARANS algorithm is used and enhanced (E-CLARANS). In this research work, the two algorithms E-CURE and E-CLARANS are combined (Hybrid) for performing a clustering process and finding the outliers in data streams. The performance of this hybrid clustering algorithm is compared with the existing hybrid clustering algorithms namely BIRCH with CLARANS and CURE with CLARANS. The performance factors used in this analysis are clustering accuracy and outlier detection accuracy. By analyzing the experimental results, it is observed that the proposed hybrid clustering approach E-CURE with E-CLARANS performance is more accurate than the existing hybrid clustering algorithms.