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.

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.

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.

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.

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.

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.

Ant-based clustering is a nature-inspired technique whereas stochastic agents perform the task of clustering high-dimensional data. This paper analyzes the popular technique of Lumer/Faieta. It is shown that the Lumer/Faieta approach is strongly related to Kohonen's Self- Organizing Batch Map. A unifying basis is derived in order to assess strengths and weaknesses of both techniques. The behaviour of several popular ant-based clustering techniques is explained.

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

The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2003

In this paper we will present a new clustering algorithm for unsupervised learning. It is inspired from the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. The artificial ants that we have defined will similarly build a tree. Each ant represents one data. The way ants move and build this tree depends on the similarity between the data. We have compared our results to those obtained by the k-means algorithm and by AntClass on numerical databases (either artificial, real, or from the CE.R.I.E.S.). We show that AntTree significantly improves the clustering process.

By classifying the enormous quantity of data, the structure and the relativity that lurks among data can be found. As a data classifying technique, the cluster analysis techniques are well known. However, because those techniques include some defects, the development of a new technique is necessary. Recently, Ant Colony Clustering (ACC) technique using ant swarm intelligence was proposed by Erik D. Lumer. The swarm intelligence is a property that each of individuals acts a simple behavior and exchange information each other, and the entire group makes the best behavior. ACC algorithm tries to overcome the defects of existing techniques by using this swarm intelligence idea. In this research, we propose a new algorithm that improves the efficiency of clustering in Lumer’s ACC algorithms. Numerical experiments show the improvement is effective.

Lecture Notes in Computer Science, 2004

Various clustering methods based on the behaviour of real ants have been proposed. In this paper, we develop a new algorithm in which the behaviour of the artificial ants is governed by fuzzy IF-THEN rules. Our algorithm is conceptually simple, robust and easy to use due to observed dataset independence of the parameter values involved.

International Journal of Data Mining & Knowledge Management Process, 2012

Clustering analysis is an important function of data mining. There are various clustering methods in Data Mining. Based on these methods various clustering algorithms are developed. Ant-clustering algorithm is one of such approaches that perform cluster analysis based on "Swarm Intelligence'. Existing antclustering algorithm uses two user defined parameters to calculate the picking-up probability and dropping probability those are used to form the cluster. But, use of user defined parameters may lead to form an inaccurate cluster. It is difficult to anticipate about the value of the user defined parameters in advance to form the cluster because of the diversified characteristics of the dataset. In this paper, we have analyzed the existing ant-clustering algorithm and then numerical analysis method of linear equation is proposed based on the characteristics of the dataset that does not need any user defined parameters to form the clusters. Results of numerical experiments on synthetic datasets demonstrate the effectiveness of the proposed method.

Lecture Notes in Computer Science, 2012

This paper presents a work inspired by the Pachycondyla apicalis ants behavior for the clustering problem. These ants have a simple but efficient prey search strategy: when they capture their prey, they return straight to their nest, drop off the prey and systematically return back to their original position. This behavior has already been applied to optimization, as the API meta-heuristic. API is a shortage of api-calis. Here, we combine API with the ability of ants to sort and cluster. We provide a comparison against Ant clustering Algorithm and K-Means using Machine Learning repository datasets. API introduces new concepts to ant-based models and gives us promising results.

International Journal of Computer Applications, 2012

Clustering is a data mining technique for the analysis of data in various areas such as pattern recognition, image processing, information science, bioinformatics etc. Hierarchical clustering techniques form the clusters based on top-down and bottom-up approaches. Hierarchical agglomerative clustering is a bottom-up clustering method. Ant based clustering methods form clusters by picking and dropping the objects according to surroundings. This paper proposes an agglomerative clustering algorithm, AGG_ANTS based on ant colonies. AGG_ANTS clusters the objects by moving ants on the grid and merging their loads according to similarity resulting in bigger clusters. It avoids the calculation of similarity in the surrounding and pick/drop of objects again and again resulting in a more efficient algorithm.