TY - JOUR
T1 - MMR
T2 - An algorithm for clustering categorical data using Rough Set Theory
AU - Parmar, Darshit
AU - Wu, Teresa
AU - Blackhurst, Jennifer
PY - 2007/12
Y1 - 2007/12
N2 - A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today's databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min-Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.
AB - A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today's databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min-Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.
KW - Categorical data
KW - Cluster analysis
KW - Data mining
KW - Rough Set Theory
UR - http://www.scopus.com/inward/record.url?scp=34548666399&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548666399&partnerID=8YFLogxK
U2 - 10.1016/j.datak.2007.05.005
DO - 10.1016/j.datak.2007.05.005
M3 - Article
AN - SCOPUS:34548666399
SN - 0169-023X
VL - 63
SP - 879
EP - 893
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
IS - 3
ER -