A novel distance measure for data vectors with nominal feature values

Authors:
Humar Kahramanli
Affiliations:
-
Venue:
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Year:
2010

Citing 4
Cited 0

Accurate and Reliable Diagnosis and Classification Using Probabilistic Ensemble Simplified Fuzzy ARTMAP

IEEE Transactions on Knowledge and Data Engineering
An Efficient Hybrid Algorithm for Data Clustering Using Improved Genetic Algorithm and Nelder Mead Simplex Search

ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer

Digital Signal Processing
Predicting breast cancer survivability: a comparison of three data mining methods

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The selection right distance measure is important for most machine learning algorithms. Euclidean distance is a commonly used distance measure in many methods due to simplicity of implementation. However the properties of problem domain are important thing, this selection must be done carefully. For example, most problems use data vectors with real-valued and nominal feature values. Euclidian distance produces reasonable results for real data, whereas it can not be said for nominal data. Hence in this study the new distance measure has been proposed for calculating distance between data vectors with nominal feature value. As the testing K-Means Clustering algorithm and the Mammographic Mass Data form UCI Repository have been used.