Name
Abstract | ||
|---|---|---|
This paper introduces a relational topological map model, dedicated to multidimensional categorial data (or qualitative data) arising in the form of a binary matrix or a sum of binary matrices. This approach is based on the principle of Kohonen's model (conservation of topological order) and uses the Relational Analysis formalism by maximizing a modified Condorcet criterion. This proposed method is developed from the classical Relational Analysis approach by adding a neighborhood constraint to the Condorcet criterion. We propose a hybrid algorithm, which deals linearly with large data sets, provides a natural clusters identification and allows a visualization of the clustering result on a two-dimensional grid while preserving the a priori topological order of this data. The proposed approach called Relational Topological Map (RTM) was validated on several databases and the experimental results showed very promising performances. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1142/S146902681000294X | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Relational analysis, Condorcean clustering, self-organizing map, qualitative data | Hybrid algorithm,Logical matrix,Topological order,Computer science,Grey relational analysis,Condorcet criterion,Self-organizing map,Artificial intelligence,Topological map,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1469-0268 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lazhar Labiod | 1 | 34 | 13.50 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Younès Bennani | 3 | 269 | 53.18 |