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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title: Imputation of Missing Values using Association Rule Mining & K-Mean Clustering
Authors Name: Sweety Baiwal , Abhishek Raghuvanshi
Unique Id: IJSDR1608043
Published In: Volume 1 Issue 8, August-2016
Abstract: The data mining architecture works on facts and figures which are used for any type of decision making. To perform any analysis and decision making, these facts must be complete so that the analyst can make a strategy for decision making. In fact the most important problem in knowledge discovery is the missing values of the attributes of the Dataset. The presence of such imperfections usually requires a preprocessing stage in which the data are prepared and cleaned, in order to be useful, and sufficiently clear for the knowledge extraction process. In this paper we are created hybrid approach for imputation or Replacement of the missing values. In Hybrid approach we use association rules and K-Nearest Neighbor methods. These methods can work with text dataset, Boolean dataset and with numeric dataset. We also analysis the parametric, non-parametric and semi-parametric imputation methods.
Keywords: Data Mining, Missing Values, Imputation, Feature Selection, Parametric, Non Parametric, Semi Parametric.
Cite Article: "Imputation of Missing Values using Association Rule Mining & K-Mean Clustering", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 8, page no.340 - 344, August-2016, Available :http://www.ijsdr.org/papers/IJSDR1608043.pdf
Downloads: 000101745
Publication Details: Published Paper ID: IJSDR1608043
Registration ID:160689
Published In: Volume 1 Issue 8, August-2016
DOI (Digital Object Identifier):
Page No: 340 - 344
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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