Name
Abstract | ||
|---|---|---|
Multiagent Systems consist of multiple computing elements called agents, which in order to achieve a given objective, can act on their own, react to the inputs, pro-act and cooperate. Data Mining deals with large data. Large data clustering is a data mining activity where in efficient clustering algorithms select a subset of original dataset as representative patterns. In the current work we propose a multi-agent based clustering scheme that combines multiple agents, each capable of generating a set of prototypes using an independent prototype selection algorithm. Each prototype set is used to predict the labels of unseen data. The results of these agents are combined by another agent resulting in a high classification accuracy. Such a scheme is of high practical utility in dealing with large datasets. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15470-6_13 | AMT |
Keywords | Field | DocType |
clustering scheme,large data,data mining activity,independent prototype selection algorithm,large data clustering,large datasets,high practical utility,unseen data,efficient clustering algorithm,data mining application,high classification accuracy,data mining | Canopy clustering algorithm,Data mining,Clustering high-dimensional data,CURE data clustering algorithm,Data stream mining,Data stream clustering,Affinity propagation,Correlation clustering,Computer science,Cluster analysis | Conference |
Volume | ISSN | ISBN |
6335.0 | 0302-9743 | 3-642-15469-7 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Ravindra Babu | 1 | 57 | 6.26 |
| M. Narasimha Murty | 2 | 824 | 86.07 |
| S. V. Subrahmanya | 3 | 72 | 8.72 |