Ant-based Clustering Algorithms: A Brief Survey

Jafar, O.A. Mohamed; Sivakumar, R.

doi:10.7763/ijcte.2010.v2.242

Cited by 57 publications

(32 citation statements)

References 49 publications

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“…Cluster analysis consists of grouping a set of objects (data instances) in such a way that objects in the same group (cluster) are more similar (in some sense) to each other than to those in other groups [25]. Many ACO algorithms for clustering are briefly reviewed in [6]. Amongst these various ACO algorithms for clustering, we extended the work in [7] for our clusteringbased BMN classification algorithms.…”

Section: Ant Colony Optimization Backgroundmentioning

confidence: 99%

“…ACO has been successfully employed in several research areas related to our current work, classification [2], [3], [4], [5], clustering [6], [7], [8], and learning general-purpose Bayesian Networks (BNs) [9], [10], [11], [12]. Recently, the authors have introduced ABC-Miner [13], the first ACO-based algorithm to build Bayesian network classifiers, which has shown better performance compared to some greedy and deterministic BN algorithms.…”

Section: Introduction Ant Colony Optimization (Aco)mentioning

confidence: 99%

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Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

Salama

Freitas

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local networks, typically, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Alternatively, multi-nets can be learnt upon arbitrary partitions of a dataset, in which each partition holds more consistent variable dependencies given the data subset in the partition. This paper proposes two contributions to the approach that clusters the dataset into separate data subsets to build asymmetric local BN classifiers, one for each subset. First, we extend the K-modes algorithm, previously used by the Case-Based Bayesian Network Classifiers (CBBN) approach to create clusters before learning the BN classifiers. Second, we introduce the Ant-Clust-B algorithm that employs Ant Colony Optimization (ACO) to learn clusteringbased BMNs. Ant-Clust-B uses ACO in the clustering step before learning the local BN classifiers. Empirical results are obtained from experiments on 18 UCI datasets. [12]. Recently, the authors have introduced ABC-Miner [13], the first ACO-based algorithm to build Bayesian network classifiers, which has shown better performance compared to some greedy and deterministic BN algorithms. Thus, we carry on developing ACO-based algorithms in the Bayesian classification area. Classification is a central problem in data mining and machine learning where the system builds, from labelled data instances, a model (classifier) that predicts the class of unlabelled instances [14]. There are many types of classification methods [14], but in this work we focus on building BN classifiers. I. INTRODUCTIONA BN classifier is a special kind of probabilistic networks that aims to predict the class of a data instance by computing the posterior probability of each available class value, given the values of the predictor attributes of the instance, and then labeling the instance with the class having the highest posterior probability. Naïve-Bayes, as discussed in [15], is the simplest kind of BN classifier and it assumes the attributes are independent given the class label. Although it obtained good predictive performance in several domains [15], extensions

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Section: Ant Colony Optimization Backgroundmentioning

confidence: 99%

Section: Introduction Ant Colony Optimization (Aco)mentioning

confidence: 99%

Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

Salama

Freitas

2013

2013 IEEE Congress on Evolutionary Computation

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“…A review of ant algorithms and their application is given by Jafar and Sivakumar [Jafar and Sivakumar, 2010], from which it is apparent that there are numerous confusing ant models and terminologies. This section describes ant clustering algorithms by categorising them into four types.…”

Section: Ant Algorithms and Their Application To Clustering 251 Funmentioning

confidence: 99%

A Multiple Pheromone Ant Clustering Algorithm

Chircop

Buckingham

2014

Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

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“…From other bio-inspired perspectives, ACO algorithms have also produced promising results. Kao and Cheng [10] introduced a centroid-based ACO clustering algorithm; França et al [7] introduce a bi-clustering algorithm; and Ashok and Messinger focused their work on graph-based clustering [1]; several other approaches are discussed in [9].…”

Section: Related Workmentioning

confidence: 99%

MACOC: A Medoid-Based ACO Clustering Algorithm

Menéndez

Otero

Camacho

2014

Lecture Notes in Computer Science

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Abstract. The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.

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Ant-based Clustering Algorithms: A Brief Survey

Cited by 57 publications

References 49 publications

Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

A Multiple Pheromone Ant Clustering Algorithm

MACOC: A Medoid-Based ACO Clustering Algorithm

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