Missing values and incomplete data are usual occurrences in real datasets [1]. The problem of recovering missing values from a dataset has become an important research issue in the field of data mining and machine learning [2]. With the speedy increase in the use of databases, the difficulty of missing values unavoidably arises. The techniques developed to effectively recover these missing values should be highly accurate in order to remove the missing values completely. The association rules are the popular method that is effectively used to establish the relationship among items in databases. The discovered association rules are useful to recover the missing values in databases. There are several methods proposed to surmount the problem of missing value [4]. In this paper we present a study over the existing methods.
Database is a collection of tables of data items, if the database is organized according to relational model it is called relational database. In a relational database, a logical and efficient design is just as critical. A poorly designed database may provide erroneous information, or may even fail to work properly may be difficult to use. Most of these problems are the result of two bad design features called redundant data and anomalies. Database normalization is the process of designing a database satisfying a set of integrity constraints, efficiently and in order to avoid inconsistencies when manipulating the database. Most of the research work has been devoted to functional dependencies. There are several algorithms have been developed in the past year like TANE, FD_Mine FD_Discover, Dep-Miner, FUN, FD Analysis using Rough sets, FD discovery by Bayes Net. In This paper we present a comparative study over Dep-Miner and FUN. We compare the working process of Dep-Miner and FUN using a simple example.
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