Published Paper Details:

Data mining refers to extracting or mining knowledge from large amounts of data. Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. In general, data mining tasks are classified into two categories descriptive and predictive. Descriptive mining tasks characterize the general properties of the data in the database. Predictive mining tasks perform inference on the current data in order to make predictions. Stream data flow in and out of a computer system continuously and with varying update rates. They are temporally ordered, fast changing, massive, and potentially infinite. It may be impossible to store an entire data stream or to scan through it multiple times due to its tremendous volume. In distributed systems, database systems and data mining the managing and processing of data stream is an active research area. Data Streams are highly dynamic nature due to high data distribution. Data streams are applied in network monitoring, tele communication systems and sensor networks. In online monitoring time and space utilization should be very efficient. My quest analyses and classifies application of diverse data mining techniques in efficient aspects of data stream mining. A solution is designed to map data mining techniques to data stream mining challenges and requirements.