Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model
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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Mehran University of Engineering and Technology
2016-10-01
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Series: | Mehran University Research Journal of Engineering and Technology |
Subjects: | |
Online Access: | http://publications.muet.edu.pk/research_papers/pdf/pdf1427.pdf |
Summary: | 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the stream and to efficiently remove outdated contents from the data stream.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 |
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ISSN: | 0254-7821 2413-7219 |