Ant-based sorting and ACO-based clustering approaches: A review

Jabbar, Ayad Mohammed; Ku-Mahamud, Ku Ruhana; Sagban, Rafid

doi:10.1109/iscaie.2018.8405473

Cited by 16 publications

(12 citation statements)

References 51 publications

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“…According to [23], the ant algorithm for clustering can be divided into two groups, ant-based sorting and ACO based clustering. Ant-based sorting algorithm uses two dimensions grid (x, y) plane.…”

Section: Related Workmentioning

confidence: 99%

“…Although the ant-based sorting does not need a cluster number to be defined at priori, it needs post-processing to identify the generated clusters and requires high processing time [23]. That was proven in some studies where the cluster number should be analyzed visually after the clustering was done [19].…”

Section: Related Workmentioning

confidence: 99%

“…Although the cluster number must be defined at priori, ACO based clustering is more efficient in computation than ant-based sorting. Also, it does not need post-processing after the clustering is done [23]. Aside of ACO, some of the proposed clustering algorithms also use the same concept of solution string as ACO based clustering [12], [24]- [26].…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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Ant colony optimization (ACO) is one of robust algorithms for solving optimization problems, including clustering. However, high and redundant computation is needed to select the proper cluster for each object, especially when the data dimensionality is high, such as social media comments. Reducing the redundant computation may cut the execution time, but it can potentially decrease the quality of clustering. With the basic idea that nearby objects tend to be in the same cluster, the nearest neighbors method can be used to choose the appropriate cluster for some objects efficiently by considering their neighbor's cluster. Therefore, this paper proposes the combination of nearest neighbors and ant colony optimization for clustering (NNACOC) which can reduce the computation time but is still able to retain the quality of clustering. To evaluate its performance, NNACOC was tested using some benchmark datasets and twitter comments. Most of the experiments show that NNACOC outperformed the original ant colony optimization for clustering (ACOC) in quality and execution time. NNACOC also yielded a better result than k-means when clustering the twitter comments. Povzetek: Predstavljen je hibridni algoritem najbližjih sosedov z optimizacijo mravljinčnih kolonij za grupiranje komentarjev socialnih medijev.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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“…This technique differs from the classification learning technique (Al-Behadili et al, 2018a;Jabbar, 2018;Wahid and Al-Mazini, 2018). Different clustering algorithms aim to classify data as clusters, in which each cluster has a set of members that share similar characteristics and are dissimilar to the features of the members of other clusters (Jabbar et al, 2018). The two approaches that can be used to perform clustering are the deterministic and stochastic approaches.…”

Section: Introductionmentioning

confidence: 99%

Modified ACS Centroid Memory for Data Clustering

Jabbar¹,

Ku-Mahamud²,

Sagban³

2019

Journal of Computer Science

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Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm's memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant's movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.

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“…Data mining involves methods used at the intersection of a multidisciplinary field, and it aims to discover knowledge from databases. Data mining tasks can be categorized into classification, clustering, regression, and association [1][2][3][4]. Classification involves introducing a classification model from a database by finding the relationship between data attributes and their classes.…”

Section: Introductionmentioning

confidence: 99%

Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification

Al-Behadili

Ku-Mahamud

Sagban

2019

IJEECS

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Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, whereas exploration aims towards new promising regions during a search process. The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and computational time. The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.

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Ant-based sorting and ACO-based clustering approaches: A review

Cited by 16 publications

References 51 publications

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Modified ACS Centroid Memory for Data Clustering

Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification

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