Loading...
Please wait, while we are loading the content...
Similar Documents
Extending k-Representative Clustering Algorithm with an Information Theoretic-based Dissimilarity Measure for Categorical Objects
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nguyen, Thu-Hien Thi Huynh, Van-Nam |
| Copyright Year | 2013 |
| Abstract | This paper aims at introducing a new dissimilarity measure for categorical objects into an extension of k-representative algorithm for clustering categorical data. Basically, the proposed dissimilarity measure is based on an information theoretic definition of similarity introduced by Lin [15] that considers the amount of information of two values in the domain set. In order to demonstrate the efficiency of the extended k-representative algorithm with the new dissimilarity measure, we conduct a series of experiments on real datasets from UCI Machine Learning Repository and compare the result with several previously developed algorithms for clustering categorical data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://saki.siit.tu.ac.th/acis2013/uploads_final/114__9cbb4dbdc0225f2e0e695b1d0b6146a5/acis2013.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
