Abstract
Clustering algorithms partition data sets into of objects such that the pairwise similarity objects within the same cluster is higher than assigned to different clusters. Defining a measure becomes challenging in the of categorical data and affects the quality meaningfulness of the clusters formed. , the curse of dimensionality diminishes robustness of such measures. This paper introduces (Supervised Clustering with Association Rules) nontraditional algorithm for clustering massive high categorical data. SCAR is robust to the of dimensionality, it relies on association rules an intuitive way to evaluate the similarity between objects and group them.
| Original language | English (US) |
|---|---|
| Title of host publication | 2008 International Conference on Innovations in Information Technology, IIT 2008 |
| Pages | 223-227 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2008 |
| Event | 2008 International Conference on Innovations in Information Technology, IIT 2008 - Al Ain, United Arab Emirates Duration: Dec 16 2008 → Dec 18 2008 |
Other
| Other | 2008 International Conference on Innovations in Information Technology, IIT 2008 |
|---|---|
| Country | United Arab Emirates |
| City | Al Ain |
| Period | 12/16/08 → 12/18/08 |
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ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Software
Cite this
Clustering massive categorical data with class association rules. / Berrado, Abdelaziz; Runger, George.
2008 International Conference on Innovations in Information Technology, IIT 2008. 2008. p. 223-227 4781693.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Clustering massive categorical data with class association rules
AU - Berrado, Abdelaziz
AU - Runger, George
PY - 2008
Y1 - 2008
N2 - Clustering algorithms partition data sets into of objects such that the pairwise similarity objects within the same cluster is higher than assigned to different clusters. Defining a measure becomes challenging in the of categorical data and affects the quality meaningfulness of the clusters formed. , the curse of dimensionality diminishes robustness of such measures. This paper introduces (Supervised Clustering with Association Rules) nontraditional algorithm for clustering massive high categorical data. SCAR is robust to the of dimensionality, it relies on association rules an intuitive way to evaluate the similarity between objects and group them.
AB - Clustering algorithms partition data sets into of objects such that the pairwise similarity objects within the same cluster is higher than assigned to different clusters. Defining a measure becomes challenging in the of categorical data and affects the quality meaningfulness of the clusters formed. , the curse of dimensionality diminishes robustness of such measures. This paper introduces (Supervised Clustering with Association Rules) nontraditional algorithm for clustering massive high categorical data. SCAR is robust to the of dimensionality, it relies on association rules an intuitive way to evaluate the similarity between objects and group them.
UR - http://www.scopus.com/inward/record.url?scp=67649511993&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67649511993&partnerID=8YFLogxK
U2 - 10.1109/INNOVATIONS.2008.4781693
DO - 10.1109/INNOVATIONS.2008.4781693
M3 - Conference contribution
AN - SCOPUS:67649511993
SN - 9781424433971
SP - 223
EP - 227
BT - 2008 International Conference on Innovations in Information Technology, IIT 2008
ER -