Method presented for finding Frequent Itemsets in web data streams

Q3 Computer Science

International Journal of Advances in Soft Computing and its Applications Pub Date : 2016-01-01 DOI:10.5899/2016/JSCA-00065

Farzaneh Kaviani, M. Khayyambashi

引用次数: 0

Abstract

Continual data checking is considered as one of the most common search tools for frequent itemsets which requires storage on memory. On the other hand, according to properties of data stream which are unlimited productions with a high-speed, it is not possible saving these data on memory and we need for techniques which enables online processing and finding repetitive standards. One of the most popular techniques in this case is using sliding windows. The benefits of these windows can be reducing memory usage and also search acceleration. In this article, a new vertical display and an algorithm is provided based on the pins in order to find frequent itemsets in data streams. Since this new display has a compressed format itself so, the proposed algorithm in terms of memory consumption and processing is more efficient than any other similar algorithms.

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提出了在web数据流中查找频繁项集的方法

连续数据检查被认为是最常用的搜索工具之一，频繁的项目集需要存储在内存上。另一方面，由于数据流是高速无限生产的特性，不可能将这些数据保存在内存中，我们需要能够在线处理和查找重复标准的技术。在这种情况下，最流行的技术之一是使用滑动窗口。这些窗口的好处是可以减少内存使用和加速搜索。在本文中，提供了一种新的垂直显示和一种基于引脚的算法，以便在数据流中查找频繁的项集。由于这种新显示本身具有压缩格式，因此所提出的算法在内存消耗和处理方面比任何其他类似算法都更有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Advances in Soft Computing and its Applications Computer Science-Computer Science Applications

CiteScore

3.30

自引率

0.00%

发文量

期刊介绍： The aim of this journal is to provide a lively forum for the communication of original research papers and timely review articles on Advances in Soft Computing and Its Applications. IJASCA will publish only articles of the highest quality. Submissions will be evaluated on their originality and significance. IJASCA invites submissions in all areas of Soft Computing and Its Applications. The scope of the journal includes, but is not limited to: √ Soft Computing Fundamental and Optimization √ Soft Computing for Big Data Era √ GPU Computing for Machine Learning √ Soft Computing Modeling for Perception and Spiritual Intelligence √ Soft Computing and Agents Technology √ Soft Computing in Computer Graphics √ Soft Computing and Pattern Recognition √ Soft Computing in Biomimetic Pattern Recognition √ Data mining for Social Network Data √ Spatial Data Mining & Information Retrieval √ Intelligent Software Agent Systems and Architectures √ Advanced Soft Computing and Multi-Objective Evolutionary Computation √ Perception-Based Intelligent Decision Systems √ Spiritual-Based Intelligent Systems √ Soft Computing in Industry ApplicationsOther issues related to the Advances of Soft Computing in various applications.