TY - GEN
T1 - Team building by data clustering with constraints
AU - Yang, Chao Lung
AU - Irfana, Maisyatus S.
AU - Samopa, Febriliyan
PY - 2014
Y1 - 2014
N2 - Making cooperative teams from multiple participants is a common exercise during the beginning of teamwork collaboration. Due to the priori knowledge among participants, several team building constraints can be formed. For example, it might be required to place participants from the same organization in the same group. In this research, we consider the cooperative team building as a constrained clustering problem. Clustering algorithms equipped with instance level must-link (ML) and cannon-link (CL) constraints are known to improve the efficiency and accuracy on handling data clustering. This paper presents the result of applying the constrained clustering algorithm to accommodate the complete must-link (CML) constraints, a special case of ML constraints, in which all participants should be pre-grouped with other members. The result shows the CML clustering can be used to group teams by pre-group requirements with promising computational performance.
AB - Making cooperative teams from multiple participants is a common exercise during the beginning of teamwork collaboration. Due to the priori knowledge among participants, several team building constraints can be formed. For example, it might be required to place participants from the same organization in the same group. In this research, we consider the cooperative team building as a constrained clustering problem. Clustering algorithms equipped with instance level must-link (ML) and cannon-link (CL) constraints are known to improve the efficiency and accuracy on handling data clustering. This paper presents the result of applying the constrained clustering algorithm to accommodate the complete must-link (CML) constraints, a special case of ML constraints, in which all participants should be pre-grouped with other members. The result shows the CML clustering can be used to group teams by pre-group requirements with promising computational performance.
KW - Agglomerative Hierarchical Clustering
KW - Constrained Clustering Algorithm
KW - Cooperative Team Building
KW - K-means
UR - http://www.scopus.com/inward/record.url?scp=84904655771&partnerID=8YFLogxK
U2 - 10.1109/CSCWD.2014.6846876
DO - 10.1109/CSCWD.2014.6846876
M3 - Conference contribution
AN - SCOPUS:84904655771
SN - 9781479937769
T3 - Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2014
SP - 390
EP - 395
BT - Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2014
PB - IEEE Computer Society
T2 - 2014 18th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2014
Y2 - 21 May 2014 through 23 May 2014
ER -