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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.

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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 -

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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

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