Open Access   Article Go Back

Decision Trees for Mining Data Streams Based on the Gaussian Approximation

S.Babu 1 , G.Fathima 2

Section:Review Paper, Product Type: Journal Paper
Volume-4 , Issue-3 , Page no. 35-38, Mar-2016

Online published on Mar 30, 2016

Copyright © S.Babu, G.Fathima . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S.Babu, G.Fathima , “Decision Trees for Mining Data Streams Based on the Gaussian Approximation,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.35-38, 2016.

MLA Style Citation: S.Babu, G.Fathima "Decision Trees for Mining Data Streams Based on the Gaussian Approximation." International Journal of Computer Sciences and Engineering 4.3 (2016): 35-38.

APA Style Citation: S.Babu, G.Fathima , (2016). Decision Trees for Mining Data Streams Based on the Gaussian Approximation. International Journal of Computer Sciences and Engineering, 4(3), 35-38.

BibTex Style Citation:
@article{_2016,
author = {S.Babu, G.Fathima },
title = {Decision Trees for Mining Data Streams Based on the Gaussian Approximation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2016},
volume = {4},
Issue = {3},
month = {3},
year = {2016},
issn = {2347-2693},
pages = {35-38},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=823},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=823
TI - Decision Trees for Mining Data Streams Based on the Gaussian Approximation
T2 - International Journal of Computer Sciences and Engineering
AU - S.Babu, G.Fathima
PY - 2016
DA - 2016/03/30
PB - IJCSE, Indore, INDIA
SP - 35-38
IS - 3
VL - 4
SN - 2347-2693
ER -

VIEWS PDF XML
1742 1612 downloads 1678 downloads
  
  
           

Abstract

Decision Trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have dealt with the issue of growing a decision tree from available data. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified or time-consuming. The primary comparison parameters are time and accuracy. Also shown efforts made for handling the change in the concept and they are compared in terms of memory, technique and accuracy. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.

Key-Words / Index Term

Data steam, decision trees, information gain, Gaussian approximation

References

[1] Caiyan Dai and Ling Chen, "An Algorithm for Mining Frequent Closed Itemsets with Density from Data Streams", International Journal of Computer Sciences and Engineering, Volume-04, Issue-02, Page No (40-48), Feb -2016, E-ISSN: 2347-2693
[2] P. Argentiero, R. Chin and P. Beaudet, "An automated approach to the design of decision tree classifiers," IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 51-57 (1982).
[3] P. Fletcher and M.j.D. Powell,"A rapid decent method for minimization," Computer Journal, Vol.6, ISS.2, 163-168 (1963).
[4] Rudolf Ahlsmede and Ingo Wegeru, Search problems, Wiley-Interscience, 1987.
[5] K.S. Fu, Sequential methods in pattern recognition and machine learning, Academic press, 1998.
[6] D. E. Gustafson, S. B. Gelfand, and S. K. Mitter, “ A nonparametric multiclass partitioning methods for classification,” in proc. 5th int. conf. pattern Recognition, 654-659 (1980).
[7] E. G. Henrichon,Jr. and K. S. Fu, "A nonparametric partitioning procedure for pattern classification," IEEE Trans. Computer., Vol. C-18, 604-624,(1969).
[8] G. R. Dattatreya and V. V. S. Sarma,"Bayesian and decision tree approaches for pattern recognition including feature measurement costs," IEEE Trans. Pattern Anal. Mach. Intell. Vol. PAMI-3, 293-298, (1981).
[9] R. L. P. Chang and T. Pavlidis, "Fuzzy decision tree algorithms," IEEE Trans Syst. Man Cybernet., vol. SMC-7, 28-35 (1977)
[10] J. Aczel and J. Daroczy, On measures of information and their characterizations, New York: Academic, 1975.