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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Berrado, A. Runger, G. |
| Copyright Year | 2008 |
| Description | Author affiliation: Arizona State Univ., Tempe, AZ (Runger, G.) || Al Akhawayn Univ. (Berrado, A.) |
| Abstract | Clustering algorithms partition data sets into groups of objects such that the pairwise similarity between objects within the same cluster is higher than those assigned to different clusters. Defining a similarity measure becomes challenging in the presence of categorical data and affects the quality and meaningfulness of the clusters formed. Furthermore, the curse of dimensionality diminishes the robustness of such measures. This paper introduces SCAR (supervised clustering with association rules) a nontraditional algorithm for clustering massive high dimensional categorical data. SCAR is robust to the curse of dimensionality, it relies on association rules as an intuitive way to evaluate the similarity between objects and group them. |
| Starting Page | 223 |
| Ending Page | 227 |
| File Size | 347857 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424433964 |
| DOI | 10.1109/INNOVATIONS.2008.4781693 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-16 |
| Publisher Place | United Arab Emirates |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering methods Supervised learning Clustering algorithms Euclidean distance Robustness Entropy Partitioning algorithms Topology Association rules Mutual information |
| Content Type | Text |
| Resource Type | Article |
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