A Framework for Filling of Missing Data: an Imputation Method for Mining and With an Empirical Analysis
K.Fayaz
Dr. K.Fayaz, Department of Computer Science & Technology, S.K. University, Ananthapuram (A.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 1209-1217 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3376038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Missing data causes a variety of problems in data analysis. First, lost data decrease statistical power. Statistical power refers to the ability of an analytic technique to detect a significant effect in a data set. Second, missing data produce biases in parameter estimates and can make the analysis harder to conduct and the results harder to present. To overcome the missing data problem, a Component-based Framework for imputation is proposed, which efficiently searches the most plausible value for replacement [2]. The algorithm developed ensures complete imputation, the first phase of imputation is based on the complexity of 1) finding the missing value entry, 2) select the category of attribute set, 3) generate the characteristic weight, 4) search the characteristic weight, 5) find the location of missing value and 6) replace the plausible value with missing value.
Keyword: Missing Values, Imputation, Data Mining, Weight Functions, Component Framework.
Scope of the Article: Data Analytics