Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
On Improving Clustering in Numerical Databases with Artificial Ants
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
A grid portal for solving geoscience problems using distributed knowledge discovery services
Future Generation Computer Systems
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Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups, called clusters. Traditional clustering methods require that all data have to be located at the site where they are analyzed and cannot be applied in the case of multiple distributed datasets. This paper describes a multi-agent algorithm for clustering distributed data in a peer-to-peer environment. The algorithm proposed is based on the biology-inspired paradigm of a flock of birds. Agents, in this context, are used to discovery clusters using a density-based approach. Swarm-based algorithms have attractive features that include adaptation, robustness and a distributed, decentralized nature, making them well-suited for clustering in p2p networks, in which it is difficult to implement centralized network control. We have applied this algorithm on synthetic and real world datasets and we have measured the impact of the flocking search strategy on performance in terms of accuracy and scalability.