Data Mining Methods for Large Data

Authors

  • P. Jose
  • P. Arumugam

DOI:

https://doi.org/10.37591/rtpl.v4i3.1325

Abstract

Abstract

High dimensional data order ends up plainly difficult assignment since information are substantial, complex to deal with, heterogeneous and progressive. Keeping in mind the end goal to lessen the informational index without influencing the classifier precision. The element determination assumes a fundamental part in huge datasets and which expands the proficiency of arrangement to pick the vital highlights for high dimensional grouping, when those highlights are superfluous or corresponded. Along these lines highlight determination is considered to use in preprocessing before applying classifier to an informational collection. Hence this great decision of highlight choice prompts the high characterization precision and limit computational cost. In spite of the fact that various types of highlight determination strategies are explore for choosing and fitting highlights, the best calculation ought to be wanted to augment the exactness of the characterization. In this paper beginning subset choice depends on the Integration of PSO and DT. The Novel approach intended to accelerate the preparation time and advance the SVM classifier precision consequently. The proposed show used to choose least number of highlights and giving high order exactness of vast datasets. 

Keywords: Feature selection; decision Tree, classification, PSO, SVM

Cite this Article
P. Arumugam, P. Jose. Data Mining Methods for Large Data. Recent Trends in Programming Languages.2017;4(3):1–6p.

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Published

2018-03-21

Issue

Vol. 4 No. 3 (2017)

Section

Research Articles

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