Finding groups in data: Cluster analysis with ants

Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. 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. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan

Risk identification of PPP mode in stadiums and gymnasiums by ant colony algorithm

Aiming at the problem that the PPP model of stadiums and gymnasiums can not realize multi-dimensional risk analysis and the global risk identification ability is poor, an ant colony algorithm is proposed. Firstly, k clustering is used to cluster PPP mode data. Then, the ant colony algorithm is analyzed stochastically, and the risk set of PPP mode is obtained, which lays the foundation for later risk identification. Finally, through the random fusion function, the PPP mode risk set based on the path profile is constructed, and the optimal identification path is obtained, so as to improve the identification accuracy of PPP mode risk. MATLAB simulation shows that the ant colony algorithm proposed in this paper is superior to the risk clustering method in the overall risk identification accuracy and identification time of PPP mode. Therefore, the ant colony algorithm proposed in this paper can be used to identify the risk of PPP mode, to meet the needs of the construction of stadiums and gymnasiums

A hierarchical approach to improve the ant colony optimization algorith

The ant colony optimization algorithm (ACO) is a fast heuristic-based method for finding favorable solutions to the traveling salesman problem (TSP). When the data set reaches larger values however, the ACO runtime increases dramatically. As a result, clustering nodes into groups is an effective way to reduce the size of the problem while leveraging the advantages of the ACO algorithm. The method for recombining groups of nodes is explored by treating the graph as a hierarchy of clusters, and modifying the original ACO heuristic to operate on a hypergraph. This method of using hierarchical clustering is significantly faster than the original ACO algorithm, even when normal clustering techniques are applied, while producing improved tour lengths

Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets

Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies in this area, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets. Dense datasets are featured by areas of higher density, where there are significantly more data instances than in the rest of the search space. This paper presents an extension of a previous algorithm named Spectral-based Ant Colony Optimization Clustering (SACOC), a spectral-based clustering methodology used for manifold identification. This work focuses on improving the SACOC algorithm through the Nystrom extension in order to deal with dense data problems. We evaluated the performance of the proposed approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several benchmark datasets

Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters

Balancing exploration and exploitation in ACS algorithms for data clustering

Ant colony optimization (ACO) is a swarm algorithm inspired by different behaviors of ants. The algorithm minimizes deterministic imperfections by assuming the clustering problem as an optimization problem. A balanced exploration and exploitation activity is necessary to produce optimal results. ACO for clustering (ACOC) is an ant colony system (ACS) algorithm inspired by the foraging behavior of ants for clustering tasks. The ACOC performs clustering based on random initial centroids, which are generated iteratively during the algorithm run. This makes the algorithm deviate from the clustering solution and performs a biased exploration. This study proposes a modified ACOC called the population ACOC (P-ACOC) to address this issue. The proposed P-ACOC allows the ants to process and update their own centroid during the algorithm run, thereby intensifying the search at the neighborhood before moving to another location.However, the algorithm quickly produces a premature convergence due to the exploitation of the same clustering results during centroid update. To resolve this issue, this study proposes a second modification by adding a restart strategy that balances between the exploration and exploitation strategy in P-ACOC.Each time the algorithm begins to converge with the same clustering solution, the restart strategy is performed to change the behavior of the algorithm from exploitation to exploration. The performance of the proposed algorithm is compared with that of several common clustering algorithms using real-world datasets. The results show that the accuracy of the proposed algorithm surpasses those of other algorithms

Aco-based feature selection algorithm for classification

Dataset with a small number of records but big number of attributes represents a phenomenon called “curse of dimensionality”. The classification of this type of dataset requires Feature Selection (FS) methods for the extraction of useful information. The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. An enhanced graph clustering ant colony optimisation (EGCACO) algorithm is proposed to solve the three (3) MGCACO algorithm problems. The proposed improvement includes: (i) an ACO feature clustering method to obtain clusters of highly correlated features; (ii) an adaptive selection technique for subset construction from the clusters of features; and (iii) a genetic-based method for producing the final subset of features. The ACO feature clustering method utilises the ability of various mechanisms such as intensification and diversification for local and global optimisation to provide highly correlated features. The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. The genetic method determines the final subset, automatically, based on the crossover and subset quality calculation. The performance of the proposed algorithm was evaluated on 18 benchmark datasets from the University California Irvine (UCI) repository and nine (9) deoxyribonucleic acid (DNA) microarray datasets against 15 benchmark metaheuristic algorithms. The experimental results of the EGCACO algorithm on the UCI dataset are superior to other benchmark optimisation algorithms in terms of the number of selected features for 16 out of the 18 UCI datasets (88.89%) and the best in eight (8) (44.47%) of the datasets for classification accuracy. Further, experiments on the nine (9) DNA microarray datasets showed that the EGCACO algorithm is superior than the benchmark algorithms in terms of classification accuracy (first rank) for seven (7) datasets (77.78%) and demonstrates the lowest number of selected features in six (6) datasets (66.67%). The proposed EGCACO algorithm can be utilised for FS in DNA microarray classification tasks that involve large dataset size in various application domains

Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm

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 bio informatics 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. 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