Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A Distributed Flocking Approach for Information Stream Clustering Analysis
SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Online clustering of parallel data streams
Data & Knowledge Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
SwarmClass: A Novel Data Clustering Approach by a Hybridization of an Ant Colony with Flying Insects
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
An adaptive flocking algorithm for performing approximate clustering
Information Sciences: an International Journal
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Clustering distributed data streams in peer-to-peer environments
Information Sciences: an International Journal
A single pass trellis-based algorithm for clustering evolving data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
A swarm intelligence algorithm based game theory
International Journal of Computing Science and Mathematics
CAPSO: Centripetal accelerated particle swarm optimization
Information Sciences: an International Journal
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Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics, and an offline phase that generates the clusters by using the summary data. In this article we propose a data stream clustering method based on a multi-agent system that uses a decentralized bottom-up self-organizing strategy to group similar data points. Data points are associated with agents and deployed onto a 2D space, to work simultaneously by applying a heuristic strategy based on a bio-inspired model, known as flocking model. Agents move onto the space for a fixed time and, when they encounter other agents into a predefined visibility range, they can decide to form a flock if they are similar. Flocks can join to form swarms of similar groups. This strategy allows to merge the two phases of density-based approaches and thus to avoid the computing demanding offline cluster computation, since a swarm represents a cluster. Experimental results show that the bio-inspired approach can obtain very good results on real and synthetic data sets.