CFP last date
20 December 2024
Reseach Article

Survey on Constrained based Data Stream Mining

by Lini Susan Kurien, Sreekumar K, Minu Kk
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 16
Year of Publication: 2014
Authors: Lini Susan Kurien, Sreekumar K, Minu Kk
10.5120/18834-0348

Lini Susan Kurien, Sreekumar K, Minu Kk . Survey on Constrained based Data Stream Mining. International Journal of Computer Applications. 107, 16 ( December 2014), 12-15. DOI=10.5120/18834-0348

@article{ 10.5120/18834-0348,
author = { Lini Susan Kurien, Sreekumar K, Minu Kk },
title = { Survey on Constrained based Data Stream Mining },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 16 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number16/18834-0348/ },
doi = { 10.5120/18834-0348 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:12.769386+05:30
%A Lini Susan Kurien
%A Sreekumar K
%A Minu Kk
%T Survey on Constrained based Data Stream Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 16
%P 12-15
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In most of the real time applications data may arrive as continuous ordered sequence of items, called Data streams. The main challenge in dealing with the Data Stream is its voluminous, complex and dynamically arriving stream of data. There are certain techniques to deal with data streams, in particular, finding the frequent or sequential patterns that occur repeatedly. These results retrieve huge number of patterns, which are hard to analyze and use them, also difficult to store these results and its intermediate results. The traditional pattern mining techniques fail to give the relevant details to the user. In order to obtain that, some constraint based mining techniques, which acts as a filter to the large result set retrieved from traditional pattern mining techniques. This paper investigates different data stream mining techniques and constrained based stream mining techniques from which the user gets the required information from the data stream.

References
  1. R. Pothrast, AJ Feelders, "Classification trees for problems with monotonicity constraints",
  2. Jian Pei, JiaweiHan,"Constraint Frequent Pattern Mining: A pattern- growth view",
  3. Jian Pei, JiaweiHai, Wei Wang, "Mining Sequential Patterns with Constraint in Large Database",
  4. Carson Kai-Sang Leung, Dale A Brajczuk,"Efficient Algorithm for the Mining of Constrained Frequent Patterns from Uncertain Data"
  5. Jian Pei, Jiawei Han, Laks VS Lakshmanan "Pushing convertible constraints in Frequent Itemset Mining", Data Mining and knowledge Discovery 8, 277-252,2004, 2004 Kluwer Academic Publishers.
  6. Graham Cormode, MariosHadjcelefterion, "Methods for finding Frequent Items in Data Streams"
  7. Sandy Moens, EminAksehirli, Bart Goethals, "Frequent Itemset Mining for Big Data"
  8. Mehdi Khiari, PariceBoizumault, BrumoCremilleux, "Local Constraint-Based Mining and set Constraint Programming for Pattern Discovery"
  9. Francesco Bonchi, Claudio Lucchese, "Pushing Tougher Constraint in Frequent Pattern Mining"
  10. TaneliMielikainen, "Intersecting Data to closed sets with constraints"
  11. HetalThakkar, BarzanMozafari, Carlo Zaniolo, "Continuous Post-Mining of Association rules in a data stream Management System "
  12. Jian Pei, Jiawei Han, "Can we push more constraints into Frequent pattern mining"
  13. A Mala, F Ramesh Dhanaseelan, "Data Stream Mining algorithms- A Review of issues and existing approaches" IJCSE
  14. JochenHipp, Ulrich Guntzer, "Is Pushing constraints Deeply into the mining algorithms Really what we want - An alternative approach for association rule mining" Issues in data stream mining
  15. Andreia Silva and CláudiaAntunes "Pushing constraints into data streams", BigMine '13 Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
  16. Ms. S. RanjithaKumari, Dr. P. KrishnaKumari, S. Shylaja "Handling real time data sets using stream mining techniques",IJCSMC, Vol. 3, Issue. 10, October 2014, pg. 62 – 69
  17. Xiaonan Ji, James Bailey, Guozhu Dong "Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints"
Index Terms

Computer Science
Information Sciences

Keywords

Data stream mining Frequent Pattern Mining Constraints Sequential Pattern Mining.