TY  - JOUR
T1  - A Cluster-Based Deviation Detection Task Using the Artificial Bee Colony (ABC) Algorithm
AU - Abdulsalam, M. Faiza AU - Bakar, Azuraliza Abu 
JO  - International Journal of Soft Computing
VL  - 7
IS  - 2
SP  - 71
EP  - 78
PY  - 2012
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2012.71.78
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2012.71.78
KW  - Clustering
KW  -deviation detection
KW  -clustering-based deviation detection
KW  -Artificial Bee Colony (ABC) algorithm
KW  -intelligent
KW  -Malaysia
AB  - The Artificial Bee Colony (ABC) algorithm was motivated by the intelligent foraging behavior of honey bee swarms. The ABC algorithm was developed to solve clustering problems and revealed promising results in processing time and solution quality although, no research has yet considered employing ABC for deviation detection. In this study, researchers propose modifying the ABC clustering algorithm for deviation detection. An outlier factor has been used to identify the top n outliers that deviate from the dataset. The proposed algorithm was tested on three UCI benchmark datasets. Experimental results have shown that the ABC deviation detection algorithm has performed with comparable results.
ER  -