Name
Abstract | ||
|---|---|---|
We propose an equitable conceptual clustering approach based on multi-agent optimization, where each cluster is represented by an agent having its own satisfaction. The problem consists in finding the best cumulative satisfaction while emphasizing a fair compromise between all individual agents. The fairness goal is achieved using an equitable formulation of the Ordered Weighted Averages (OWA) operator. Experiments performed on UCI and ERP datasets show that our approach efficiently finds clusterings of consistently high quality. |
Year | Venue | Field |
|---|---|---|
2018 | PAKDD | Data mining,Computer science,Equity (finance),Artificial intelligence,Operator (computer programming),Compromise,Conceptual clustering,Machine learning,Weighted arithmetic mean |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noureddine Aribi | 1 | 0 | 2.37 |
| Abdelkader Ouali | 2 | 7 | 2.15 |
| Yahia Lebbah | 3 | 115 | 19.34 |
| Samir Loudni | 4 | 152 | 21.48 |