COMPARATIVE STUDIES OF DIFFERENT IMPUTATION METHOD FOR DIFFERENT MISSING VALUE AND PROPOSED AN EFFICIENT IMPUTATION ALGORITHM

pranab goswami

Abstract


Information plays a very important role in our life. Advances in many research fields depend on the ability of discovering knowledge in very large data bases. A lot of businesses base their success on the availability of marketing information. This kind of data is usually big, and not always easy to manage.

Scientists from different research areas have developed methods to analyze huge amounts of data and to extract useful information. These methods may extract different kinds of knowledge, depending on the data and on user requirements. In particular, one important knowledge discovery task is supervised learning. Today, there exist many methods to build classifiers, belonging to different fields, such as artificial intelligence, soft computing, and statistics.

Unfortunately, traditional methods usually cannot deal directly with real-world data, because of missing or wrong items. This report concerns the former problem: the unavailability of some values. The majority of interesting data bases is incomplete, i.e., one or more values are missing inside some records, or some records are missing at all.


Keywords


Missing at random (MAR),Missing completely,, at random (MCAR):,Not missing at random (NMAR) ,Mean Imputation

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