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
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Future Generation Computer Systems
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ant-Based Clustering and Topographic Mapping
Artificial Life
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
An aggregated clustering approach using multi-ant colonies algorithms
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A new clustering evaluation function using Renyi's information potential
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
A local-density based spatial clustering algorithm with noise
Information Systems
A survey of kernel and spectral methods for clustering
Pattern Recognition
A general grid-clustering approach
Pattern Recognition Letters
Finding groups in data: Cluster analysis with ants
Applied Soft Computing
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Entropy-based metrics in swarm clustering
International Journal of Intelligent Systems
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Kernel Entropy Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
Foraging theory for dimensionality reduction of clustered data
Machine Learning
Editorial survey: swarm intelligence for data mining
Machine Learning
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
A modified clustering algorithm based on swarm intelligence
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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Ant-based clustering is a type of clustering algorithm that imitates the behavior of ants. To improve the efficiency, increase the adaptability to non-Gaussian datasets and simplify the parameters of the algorithm, a novel ant-based clustering algorithm using Renyi Entropy (NAC-RE) is proposed. There are two aspects to application of Renyi entropy. Firstly, Kernel Entropy Component Analysis (KECA) is applied to modify the random projection of objects when the algorithm is run initially. This projection can create rough clusters and improve the algorithm's efficiency. Secondly, a novel ant movement model governed by Renyi entropy is proposed. The model takes each object as an ant. When the object (ant) moves to a new region, the Renyi entropy in its local neighborhood will be changed. The differential value of entropy governs whether the object should move or be moveless. The new model avoids complex parameters that have influence on the clustering results. The theoretical analysis has been conducted by kernel method to show that Renyi entropy metric is feasible and superior to distance metric. The novel algorithm was compared with other classic ones by several well-known benchmark datasets. The Friedman test with the corresponding Nemenyi test are applied to compare and conclude the algorithms' performance The results indicate that NAC-RE can get better results for non-linearly separable datasets while its parameters are simple.