On the Performance of Ant-Based Clustering

European Journal of Operational Research, 2007

In this paper is presented a new model for data clustering, which is inspired from the selfassembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Several algorithms have been detailed using this model (called AntTree): deterministic or stochastic algorithms that may use or not global or local thresholds. Those algorithms have been evaluated using artificial and real databases. Our algorithms obtain competitive results when compared to the Kmeans, to ANTCLASS, and to Ascending Hierarchical Clustering. AntTree has been applied to three real world applications: the analysis of human healthy skin, the on-line mining of web sites usage, and the automatic construction of portal sites.

Advanced Studies in Behaviormetrics and Data Science, 2020

An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant colony method. We performed a simulation study in order to evaluate the method with a Monte Carlo experiment that controls some sensitive parameters of the clustering problem. After some tuning of the parameters, the method has also been applied to some benchmark real-data sets. Encouraging results were obtained in nearly all cases.

2005

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bioinformatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional cont...

We use the heuristics known as ant colony optimization in the partitioning problem for improving solutions of k-means method. Each ant in the algorithm is associated to a partition, which is modified by the principles of the heuristics; that is, by the random selection of an element, and the assignment of another element which is chosen according to a probability that depends on the pheromone trail (related to the overall criterion: the maximization of the between-classes variance), and a local criterion (the distance between objects). The pheromone trail is reinforced for those objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithm, tabu search and k-means. Results are as good as the best of the above methods.

2010

Among the many bio–inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. Clustering with swarm–based algorithms is emerging as an alternative to more conventional clustering methods, such as k–means algorithm. This proposed approach mimics the clustering behavior observed in real ant colonies. As a case study, this paper focuses on the behavior of clustering procedures in this new approach. The proposed algorithm is evaluated on a number of well–known benchmark data sets. Empirical results clearly show that the ant clustering algorithm (ACA) performs well when compared to other techniques.

Informatica, 2005

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bioinformatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.

Artificial Life, 2006

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real...

2009 IEEE International Conference on Systems, Man and Cybernetics, 2009

Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields. When there is a need to learn the inherent grouping structure of data in an unsupervised manner, ant-based clustering stand out as the most widely used group of swarm-based clustering algorithms. Under this perspective, this paper presents a new Adaptive Ant-based Clustering Algorithm (AACA) for clustering data sets. The algorithm takes into account the properties of aggregation pheromone and perception of the environment together with other modifications to the standard parameters that improves its convergence. The performance of AACA is studied and compared to other methods using various patterns and data sets. It is also compared to standard clustering using a set of analytical evaluation functions and a range of synthetic and real data collection. Experimental results have shown that the proposed modifications improve the performance of ant-colony clustering algorithm in term of quality and run time.

Journal of Computer Science and Technology, 2005

Clustering task aims at the unsupervised classi- fication of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been pro- posed different alternatives to cope with cluster- ing, there also exists an interesting and novel field of research from which different bio-inspired al- gorithms have

2011

This paper presents a new algorithm for clustering which is called an “efficient ant colony optimization clustering algorithm” (EACOC) based on a classic algorithm “LF algorithm”. We have proved the algorithm efficiency when dealt with a big variety of different data as well as providing high quality and converging speed simultaneously. This is considered as the outcome of many changes we have made including redefining the digital manner of ants, setting new formula to find out the degree of similarity and measuring the distance between objects; as well as creating a process to determine the degree of similarity between the collections resulting from the repeated processes. Experimental results show, by using clustering benchmarks indicate, that this suggested algorithm is the best of (LF) Algorithm, as it could defeat the defects found in (LF) involving; the law converging speed and the big number of repeating processes.