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Articles by P. Jaganathan
Total Records ( 2 ) for P. Jaganathan
  K. Thangavel , A. Pethalakshmi and P. Jaganathan
  Database with missing values is a common phenomenon in data mining, statistical analysis, as well as in machine learning. Missing values in the database will affect the classification accuracy and effectiveness of classification rules. In this study, we have used four different methods such as Indiscernibility, Mean, Median and Mode for dealing with missing attribute values and proposed a Revised Quickreduct algorithm for dimensionality reduction. A comparative study is also performed with Revised and original Quickreduct algorithms based on the four different methods. The public domain datasets available in UCI machine learning repository with missing attribute values are used.
  Thangavel, K. , A. Pethalakshmi and P. Jaganathan
  Rough set theory introduced by Pawlak in 1982 has been applied successfully in all the fields. It creates a framework for handling imprecise and incomplete data in information systems. A Rough Set is a mathematical tool to deal with Uncertainty and vagueness of an information system. An information system can be presented as a Table with rows analogous to objects and columns analogous to attributes. Each row of the table contains values of particular attributes representing information about an object. Based on rough sets theory, this study proposes Modified Quickreduct algorithm and discusses the performance study of various reduct algorithms for constructing efficient rules. The experiments were carried out on data sets of UCI machine learning repository and the Human Immuno deficiency Virus(HIV) data set to analyze the performance study. Generally, in rule generation for taking decision from the information system, the reduct plays a vital role. The reduct algorithm that generates the least number of rules is considered an efficient one.
 
 
 
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