Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

  • Qaisar Javaid Department of Computer Science and Software Engineering, International Islamic University, Islamabad.
  • Farida Memon Department of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro.
  • Shahnawaz Talpur Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro.
  • Muhammad Arif Department of Computer Science, University of Gujrat, Gujrat.
  • Muhammad Daud Awan Faculty of Computer Science, Preston University, Islamabad.

Abstract

EPs (Extracting Frequent Patterns) from the continuous transactional data streams is a challenging and critical task in some of the applications, such as web mining, data analysis and retail market, prediction and network monitoring, or analysis of stock market exchange data. Many algorithms have been developed previously for mining FPs (Frequent Patterns) from a data stream. Such algorithms are currently highly required to develop new solutions and approaches to the precise handling of data streams. New techniques, solutions, or approaches are developed to address unbounded, ordered, and continuous sequences of data and for the generation of data at a rapid speed from data streams. Hence, extracting FPs using fresh or recent data involves the high-level analysis of data streams. We have suggested an efficient technique for the window sliding model; this technique extracts new and fresh FPs from high-speed data streams. In this study, a CPILT (Compacted Tree Compact Pattern Tree) is developed to capture the latest contents in the stream and to efficiently remove outdated contents from the data stream. The main concept introduced in this work on CPILT is the dynamic restructuring of a tree, which is helpful in producing a compacted tree and the frequency descending structure of a tree on runtime. With the help of the mining technique of FP growth, a complete list of new and fresh FPs is obtained from a CPILT using an existing window. The memory usage and time complexity of the latest FPs in high-speed data streams can efficiently be determined through proper experimentation and analysis.

Published
Oct 1, 2016
How to Cite
JAVAID, Qaisar et al. Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 35, n. 4, p. 625-644, oct. 2016. ISSN 2413-7219. Available at: <http://publications.muet.edu.pk/index.php/muetrj/article/view/407>. Date accessed: 26 may 2019. doi: http://dx.doi.org/10.22581/muet1982.1604.13.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License