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
Emergent colonization and graph partitioning
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
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
Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
PSO and ACO in optimization problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The purpose of this work is to investigate the use of new swarm intelligence based techniques for data mining. According to this approach the data mining task is constructed as a set of biologically inspired agents. Each agent represents a simple task and the success of the method depends on the cooperative work of the agents. In this paper, we present a novel algorithm that uses techniques adapted from models originating from biological collective organisms to discover clusters of arbitrary shape, size and density in spatial data. The algorithm combines a smart exploratory strategy based on the movements of a flock of birds with a shared nearest-neighbor clustering algorithm to discover clusters in parallel. In the algorithm, birds are used as agents with an exploring behavior foraging for clusters. Moreover, this strategy can be used as a data reduction technique to perform approximate clustering efficiently. We have applied this algorithm on synthetic and real world data sets and we have measured, through computer simulation, the impact of the flocking search strategy on performance.