CL-AntInc Algorithm for Clustering Binary Data Streams Using the Ants Behavior | Zendy

Nesrine Masmoudi | Zendy; Hanane Azzag | Zendy; Mustapha Lebbah | Zendy; Cyrille Bertelle | Zendy; Maher Ben Jemaa | Zendy

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CL-AntInc Algorithm for Clustering Binary Data Streams Using the Ants Behavior

Author(s) -

Nesrine Masmoudi,

Hanane Azzag,

Mustapha Lebbah,

Cyrille Bertelle,

Maher Ben Jemaa

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.08.127

Subject(s) - computer science , cluster analysis , data stream mining , data stream clustering , data mining , canopy clustering algorithm , binary number , algorithm , correlation clustering , cure data clustering algorithm , artificial intelligence , machine learning , arithmetic , mathematics

In this paper, we present a new approach using a non-hierarchical method in graph environment and the concept of artificial ants for both clustering and visualization using Tulip framework. This model can be presented to take into account data in blocks in an incremental way. It seems especially interesting to process binary data streaming. In this algorithm, we also suggest to apply swarm intelligence techniques for the incremental processing of this new challenging data type. The main novelty of this research work resides on the adaptation of CL-AntInc to perform clustering binary data streams and building growing graphs increasingly for this type of data.The proposed algorithm performance is evaluated using real world data sets extracted from Machine Learning Repository. Our algorithm is competitive when compared with other stream clustering methods

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