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Reseach Article

Mining Frequent Patterns from Data streams using Dynamic DP-tree

by Shaik Hafija, J. V. R. Murthy, Y. Anuradha, M. Chandra Sekhar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 19
Year of Publication: 2012
Authors: Shaik Hafija, J. V. R. Murthy, Y. Anuradha, M. Chandra Sekhar
10.5120/8310-1925

Shaik Hafija, J. V. R. Murthy, Y. Anuradha, M. Chandra Sekhar . Mining Frequent Patterns from Data streams using Dynamic DP-tree. International Journal of Computer Applications. 52, 19 ( August 2012), 23-27. DOI=10.5120/8310-1925

@article{ 10.5120/8310-1925,
author = { Shaik Hafija, J. V. R. Murthy, Y. Anuradha, M. Chandra Sekhar },
title = { Mining Frequent Patterns from Data streams using Dynamic DP-tree },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 19 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number19/8310-1925/ },
doi = { 10.5120/8310-1925 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:40.838872+05:30
%A Shaik Hafija
%A J. V. R. Murthy
%A Y. Anuradha
%A M. Chandra Sekhar
%T Mining Frequent Patterns from Data streams using Dynamic DP-tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 19
%P 23-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A DataStream is a real time continuous, ordered sequence of items. It is impossible to control the order in which items arrive, nor it is feasible to locally store a stream in reality. By short it is a rapid flow of continuous ordered data. By these specific characteristics static models and static two pass algorithms are not suitable to data streams. Data stream mining have following three challenges one every item is examined only once. Second the storage space should control even there is a large amount of data, third the mining results have to be produced as early as possible. In this paper we propose a novel method to mine the frequent items over data streams by dividing data as no of windows and mine frequent item sets over window using a very compact data structure DP-Tree and placing the every DP-Tree safely in disk space so that we can retrieve the tree structure for pruning as and when we require. More over we propose methods to dynamically construct and update the DP-Tree

References
  1. Pauray S. M. Tsai, 2009, Mining Frequent Itemstes in data streams using the weighted sliding window model.
  2. James Cheng , Yiping Ke, Wilfred Ng, A survey on Algorithms for Mining Frequent Itemsets over Data streams.
  3. Chris Giannella, Jiawei Han, Jian Pei, Xifeng Yan, Philip S. Yu, Mining Frequent Patterns in Data Streams at Multiple Time Granularities
  4. Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, and Young-Koo Lee CP-Tree: A Tree Structure for Single Pass Frequent Pattern Mining
  5. Chih-Hsiang Lin Ding-Ying Chiu Yi-Hung Wu, Arbee L. P. Chen Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window
  6. M. Deypir , M. H. Sadreddini ,Frequent Patterns Mining over Data Stream Using an Efficient Tree Structure.
  7. William Cheung and Osmar R. Zaiane, Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint
Index Terms

Computer Science
Information Sciences

Keywords

Data streams mining Frequent Patterns Sliding window DP-Tree (Dynamic Pattern Tree)