Abstract
A data stream mining is relevant issue in the context of information gain. The data arrived are large in amount limitless and high rate with time impractical to stock, excavating and testimony at identical measure of time to retrieve intelligence. Sliding window model utilized for frequent pattern mining data stream mining emphasis on recent data and minimum space consumed. In past algorithm window measurement change was steady to concept variation when stagnant and gets smaller when the concept variation happens. Renewed frequent patterns are moderately kept in the current concept whereas the stable transaction is moved out of window. Panes steadily combined to window and performing unnecessary mining for frequent itemsets, conduct is diminishing. Based on the sliding window model the new algorithm named KF_FSW (Kalman Filter based Flexible Sliding Window Model) which utilize Kalman filter function for prediction and measurement approach. The prediction and measurement method is done on basis of already existing information as measure. Thus coagulating the error for accurate position of behavior variation in window size fluctuates in streamed database. Test on standard dataset reports that proposed algorithm coagulates less number of windows for mining and even predicting efficiently the number of count for change ratio captured by occurring change variation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.