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

MARIA ULFAH, SIREGAR (2008) DATA PREPARATION FOR DATA MINING BASED ON NEURAL NETWORK: A CASE STUDY ON GERMAN CREDIT CLASSIFICATION DATASET. Kaunia Jurnal Sains dan Teknologi vol IV, no2, oktober 2008.

[img]
Preview
Text
MARIA ULFAH SIREGAR DATA PREPARATION FOR DATA MINING BASED ON NEURAL NETWORK A CASE STUDY ON GERMAN CREDIT CLASSIFICATION DATASET.pdf

Download (10MB) | Preview
[img] Other (Generate index codes conversion from text to indexcodes)
indexcodes.txt

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_lightbox)
lightbox.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_preview)
preview.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_medium)
medium.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_small)
small.jpg

Download (0B)
[img] Other (Generate index codes conversion from text to indexcodes)
indexcodes.txt

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_lightbox)
lightbox.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_preview)
preview.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_medium)
medium.jpg

Download (0B)
[img] Other (Thumbnails conversion from text to thumbnail_small)
small.jpg

Download (0B)

Abstract

This 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 as are given in the form of tables. Then, a data preliminary process is carried out to detect missing values from each attribute. Next, the result of statistical data analysis is displayed on charts or categories tables from each attribute. 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.
Subjects: Kaunia Jurnal
Divisions: E-Journal
Depositing User / Editor: Edi Prasetya [edi_hoki]
Date Deposited: 21 May 2013 21:28
Last Modified: 21 May 2013 21:28
URI: http://digilib.uin-suka.ac.id/id/eprint/7802

Actions (login required)

View Item View Item