A Survey on Incremental Attribute Reduction Method for Dynamic Data mining Decision Systems

Authors

  • Kumar DR Department of Computer Science and Engineering, JNTUH, Hyderabad, India
  • Rao SKM GIET, Bhubaneswar, Odisha, India
  • Rao KR Department of Computer Science and Engineering, SIET, Hyderabad, India

DOI:

https://doi.org/10.26438/ijcse/v6i12.517519

Keywords:

DIDS, mechanism in DIDS

Abstract

In dynamic data mining situations, the attribute decrease issue has three issues: variety of protest sets, variety of trait sets and variety of property estimations. For the initial two issues, a couple of accomplishments have been made. For variety of the property estimations, current characteristic decrease approaches are not productive, in light of the fact that the strategy turns into a non-incremental or wasteful one sometimes. With the end goal to address this, we initially present the idea of an irregularity degree in a deficient choice framework and demonstrate that the property decrease dependent on the irregularity degree is proportional to that dependent on the positive area. At that point, three refresh procedures of irregularity degree for dynamic fragmented choice frameworks are given. At long last, the system of the incremental attribute decrease calculation is proposed.

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Published

2018-12-31
CITATION
DOI: 10.26438/ijcse/v6i12.517519
Published: 2018-12-31

How to Cite

[1]
D. R. Kumar, S. K. M. Rao, and K. R. Rao, “A Survey on Incremental Attribute Reduction Method for Dynamic Data mining Decision Systems”, Int. J. Comp. Sci. Eng., vol. 6, no. 12, pp. 517–519, Dec. 2018.

Issue

Vol. 6 No. 12 (2018): IJCSE December Editor

Section

Survey Article

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