Imputation of Missing Values using Association Rule Mining & K-Mean Clustering
Sweety Baiwal
, Abhishek Raghuvanshi
Data Mining, Missing Values, Imputation, Feature Selection, Parametric, Non Parametric, Semi Parametric.
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.
"Imputation of Missing Values using Association Rule Mining & K-Mean Clustering", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 8, page no.340 - 344, August-2016, Available :https://ijsdr.org/papers/IJSDR1608043.pdf
Volume 1
Issue 8,
August-2016
Pages : 340 - 344
Paper Reg. ID: IJSDR_160689
Published Paper Id: IJSDR1608043
Downloads: 000347184
Research Area: Engineering
Country: Ujjain, Madhya Pradesh , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
