TAILIEUCHUNG - Handling Missing Values: Application to University Data Set

Data warehouses usually have some missing values due to unavailable data that affect the number and the quality of the generated rules. The missing values could affect the coverage percentage and number of reduces generated from a specific data set. Missing values lead to the difficulty of extracting useful information from data set. Association rule algorithms typically only identify patterns that occur in the original form throughout the database. Handling Missing Values for Association Rule Mining allows data that approximately matches the pattern to contribute toward the overall support of the pattern. This approach is also useful in processing missing data, which probabilistically contributes to the support. | International Journal of emerging trends in engineering and development Issue 1. August-2011 ISSN 2249-6149 Handling Missing Values Application to University Data Set Dinesh J. Prajapati1 Jagruti H. Prajapati2 department of Information Technology A. D. Patel institute of Technology New V. V. Nagar-388121 India. 1 Gujarat Technological University GTU . 1dinesh249@ department of Information technology Charotar Institute of Technology Changa-388421 India. 2 Charotar University of Science and Technology CHARUSAT 2jagruti_eyetea@ Abstract Data warehouses usually have some missing values due to unavailable data that affect the number and the quality of the generated rules. The missing values could affect the coverage percentage and number of reduces generated from a specific data set. Missing values lead to the difficulty of extracting useful information from data set. Association rule algorithms typically only identify patterns that occur in the original form throughout the database. Handling Missing Values for Association Rule Mining allows data that approximately matches the pattern to contribute toward the overall support of the pattern. This approach is also useful in processing missing data which probabilistically contributes to the support of possibly matching patterns. The actual data mining process deals significantly with prediction estimation classification pattern recognition and the development of association rules. Therefore the significance of the analysis depends heavily on the accuracy of the database and on the chosen sample data to be usedfor model training and testing. Keywords Data cleansing Missing values Knowledge discovery Preprocessing. 1. Introduction Missing data are the absence of data items for a subject they hide some information that may be important. In practice missing data have been one major factor affecting data quality. The presence of missing data is a general and challenging problem in the data analysis .

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