Data Mining Techniques to Fill the Missing Data and Detecting Patterns

Author(s):

D.Umamaheswari , NGM COLLEGE, POLLACHI; N.Shyamala, NGM COLLEGE, POLLACHI

Keywords:

Missing Data, Knowledge Discovery, Imputation, Patterns

Abstract:

There are a lot of serious data quality problems in real world datasets: incomplete, redundant, inconsistent and noisy. Missing data is a common issue in datamining and knowledge discovery. It is well accepted that many real-life datasets are full of missing data. However, data mining algorithms always handle missing data in very simple way. Missing data handling has become an acute issue. Inappropriate treatment will reduce the performance of data mining algorithms. For classification algorithm, its classification accuracy depends vitally on the quality of the training data. The presence of a high proportion of missing data in the training dataset is likely to render the resulting model less than reliable. Dealing with missing data has become an important issue in data mining researches and applications. This paper discusses the various imputations and sets light on new method Naive Bayesian Imputation based on Naive Bayesian classifier to estimate and replace missing data.

Other Details:

Manuscript Id	:	IJSTEV2I1021
Published in	:	Volume : 2, Issue : 1
Publication Date	:	01/08/2015
Page(s)	:	39-42

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