DATA STREAM MINING ALGORITHMS – A REVIEW OF ISSUES AND EXISTING APPROACHES A.Mala Asst.Prof.,Dept. of Information Technology, KNSK College of Engineering, Nagercoil Email: mala.elango@gmail.com F.Ramesh Dhanaseelan Prof., Dept. of Computer Applications, St. Xavier’s Catholic College of Engg.,Nagercoil Email: message_to_ramesh@yahoo.com Abstract More and more applications such as traffic modeling, military sensing and tracking, online data processing etc., generate a large amount of data streams every day. Efficient knowledge discovery of such data streams is an emerging active research area in data mining with broad applications. Different from data in traditional static databases, data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. Due to the unique features of data stream, traditional data mining techniques which require multiple scans of the entire data sets can not be applied directly to mine stream data, which usually allows only one scan and demands fast response time. Keywords Closed frequent item set, Data Streams, Frequent item set, Mining Streams, Stream Mining algorithms 1.Introduction A data stream is an ordered sequence of items that arrives in timely order. Different from data in traditional static databases, data streams [1] are continuous, unbounded, usually come with high speed and have a data distribution that often changes with time. As the number of applications on mining data streams grows rapidly, there is an increasing need to perform association rule mining on stream data. For most data stream applications, there are needs for mining frequent patterns and association rules from data streams. Some key applications in various areas are listed below. Performance monitoring: Monitor network traffic and performance, detect abnormality and intrusion, and predict frequency estimation of Internet packet streams sometimes used to find alarming incidents from data streams. Transaction monitoring: Monitor transactions in retail stores, ATM machines, and financial markets Log record mining, mine patterns from telecommunication calling records, Web server log, etc. Association rule mining can also be applied to monitor manufacturing flows [2] to predict failure or generate reports based on web log streams Sensor network mining: Mine patterns in streams coming from sensor networks or surveillance cameras and also estimate missing data [3]. 2. Data Stream Classifications Data streams can be classified into offline streams and online streams. Offline streams are characterized by regular bulk arrivals [4]. Among the above examples, generating reports based on web log streams can be treated as mining offline data streams because most of reports are made based on log data in a certain period of A.Mala et al. / International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397 Vol. 3 No. 7 July 2011 2726