Techniques for Dealing with Missing Data in Knowledge Discovery Tasks

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, 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. There exist many techniques to manage data with missing items, but no one is absolutely better then the others. Different situations require different solutions. As Allison says, “the only really good solution to the missing data problem is not to have any” [1]. This report reviews the main missing data techniques (MDTs), trying to highlight their advantages and disadvantages. Next section introduces some terminology and presents a taxonomy of MDTs. Section 3 describes these methods more in detail. Finally, some conclusions are reported.