TY - GEN
T1 - Extended K-means algorithm
AU - Yi, Faliu
AU - Moon, Inkyu
PY - 2013
Y1 - 2013
N2 - In the conventional K-means algorithm, the input data are automatically grouped into corresponding cluster by minimizing the within-cluster sum of squares. However, the traditional K-means algorithm doesn't do any constraints to the number of elements in each group. In the area of logistics management, each cluster will need to satisfy with a predefined number of elements. Thus, the clustering algorithm with controlled number of elements in each group is necessary. In this paper, we present a new method called extended k-means algorithm to extend the ordinary K-means approach. In this approach, the number of element in each group is adjusted by using greedy algorithm and the experimental results show that this extended K-means algorithm can work well for grouping data where the numbers of elements in each group need to be restrained.
AB - In the conventional K-means algorithm, the input data are automatically grouped into corresponding cluster by minimizing the within-cluster sum of squares. However, the traditional K-means algorithm doesn't do any constraints to the number of elements in each group. In the area of logistics management, each cluster will need to satisfy with a predefined number of elements. Thus, the clustering algorithm with controlled number of elements in each group is necessary. In this paper, we present a new method called extended k-means algorithm to extend the ordinary K-means approach. In this approach, the number of element in each group is adjusted by using greedy algorithm and the experimental results show that this extended K-means algorithm can work well for grouping data where the numbers of elements in each group need to be restrained.
KW - Extended k-means algorithm
KW - Greedy algorithm
KW - K-means algorithm
KW - Logistic management
KW - Pattern classification
UR - http://www.scopus.com/inward/record.url?scp=84891864942&partnerID=8YFLogxK
U2 - 10.1109/IHMSC.2013.210
DO - 10.1109/IHMSC.2013.210
M3 - Conference contribution
AN - SCOPUS:84891864942
SN - 9780769550114
T3 - Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
SP - 263
EP - 266
BT - Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
T2 - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
Y2 - 26 August 2013 through 27 August 2013
ER -