Decision Trees for Mining Data Streams Based on the Gaussian Approximation

Authors

  • Babu S Department of CSA, SCSVMV University ,Enathur, Kanchipuram, India
  • Fathima G Department of CSA, SCSVMV University ,Enathur, Kanchipuram, India

Keywords:

Data steam, decision trees, information gain, Gaussian approximation

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.

References

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Published

2025-11-11

How to Cite

[1]
S. Babu and G. Fathima, “Decision Trees for Mining Data Streams Based on the Gaussian Approximation”, Int. J. Comp. Sci. Eng., vol. 4, no. 3, pp. 35–38, Nov. 2025.

Issue

Vol. 4 No. 3 (2016): IJCSE March Edition

Section

Review Article

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