DATA PREPARATION FOR DATA MINING BASED ON NEURAL NETWORK: A CASE STUDY ON GERMAN CREDIT CLASSIFICATION DATASET

Maria Ulfah Siregar, (2009) DATA PREPARATION FOR DATA MINING BASED ON NEURAL NETWORK: A CASE STUDY ON GERMAN CREDIT CLASSIFICATION DATASET. /Jurnal/Kaunia/Volume 4, No. 2, Oktober 2008/.

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Abstract

bThis paper will give detailed data description and preparation of German Credit Classification dataset, before it is used for further processes in data mining or data warehouse. Data preparation is the longest and most difficult part of data mining process. In general, readily available data is usually dirty and sometimes no quality data is available. There are five parts in data description and preparation that are going to be given in this paper. The first part is the name of the dataset and the number of examples and their types of attributes. In the second part, some examples from good and bad class are given in the form of tables. Then, a data preliminary process is carried out to detect missing values from each of attributes. Next, the result of statistical data analysis is displayed on charts or categories tables from each of attributes. The last part is preprocessing, which comprise of data cleaning, integration and transformation. Based on the results obtained, three out of twenty attributes are deleted: Attribute 10, Attribute 18 and Attribute 20. So, the final data is smaller than the original one. Moreover, data is distributed more normally and in suitable patterns, which is hoped to be helpful for further processes.

Item Type: Article
Uncontrolled Keywords: Data preparation, data, missing value, data cleaning, data integration, data transformation, statistical analysis.
Depositing User / Editor: Users 1 not found.
Last Modified: 04 May 2012 16:39
URI: http://digilib.uin-suka.ac.id/id/eprint/713

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