Algorithms for clustering data
Algorithms for clustering data
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)
Future Generation Computer Systems
Distributed data clustering can be efficient and exact
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Information Retrieval
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
Ant-Based Clustering and Topographic Mapping
Artificial Life
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In this paper we present an improved version of the Probabilistic Ant based Clustering Algorithm for Distributed Databases (PACE). The most important feature of this algorithm is the formation of numerous zones in different sites based on corresponding user queries to the distributed database. Keywords, extracted out of the queries, are used to assign a range of values according to their corresponding probability of occurrence or hit ratio at each site. We propose the introduction of weights for individual or groups of data items in each zone according to their relevance to the queries along with the concept of familial pheromone trails as part of an Ant Odor Identification Model to bias the movements of different types of ants towards the members of their own family. Its performance is compared against PACE and other known clustering algorithms for different evaluation measures and an improvement is shown in terms of convergence speed and quality of solution obtained.