Data Mining: A Preprocessing Engine

By Luai A. Al Shalabi, Zyad Shaaban, Basel Kasasbeh. 2006

600 citations recorded by the source corpus.

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8766ea561ba91e7ff7b581882a87ed608f585b6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiexiong Tang
2. Chenwei Deng
3. Baojun Zhao
4. G. Huang
5. Kishore Kumar Sahu
6. K. Patro
7. S.Gopal
8. Lei Chen

In Information Systems

1. G. Huang
2. Kishore Kumar Sahu
3. K. Patro
4. S.Gopal

Citations in the corpus

• Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine (2015, methodology): “Before training, the input data need to be initialized by a zero-mean or z-score normalization [48]”
• Normalization: A Preprocessing Stage (2015, methodology): “Normalization is scaling technique or a mapping technique or a pre processing stage [1].”