Outlier Detection in Data Streams Using Various Clustering Approaches

Data mining is a field of computer science and information technology that deals with the discovery of hidden patterns or interesting patterns in a large or a complex database. As the dimensions of database is growing rapidly, it is necessary to analyze the huge amount of information. Nowadays, there are many applications that are generating streaming data i.e. a sequence of objects that are arriving continuously at a faster rate, for an example telecommunication, online transactions in finance, medical systems etc. Outlier detection using clustering is very difficult in data streaming because it is impossible to scan and store the streaming data multiple times, so there is a need to divide the data into data chunks. In this paper we will focus on different types of outlier detection methods in streaming data.