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
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
ACM Computing Surveys (CSUR)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A New Approach of Data Clustering Using a Flock of Agents
Evolutionary Computation
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
Split-merge incremental learning (SMILE) of mixture models
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Semi-Supervised Learning
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Today, in many clustering applications we deal with a large amount of data that are delivered in form of data streams. To be able to face the problem of analyzing the data as soon as they are produced, we need to build models that can be incrementally updated. This paper presents an adaptation of a bio-inspired algorithm that dynamically creates and visualizes groups of data, to data stream clustering. We introduce a merge operator that can summarize a group of data and a split operator that uses information of a very small set of supervised data and permits to adapt the clustering to a change in the data stream.