Modified ACS Centroid Memory for Data Clustering

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

doi:10.3844/jcssp.2019.1439.1449

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…Recent years have witnessed the use of metaheuristic algorithms such as ant colony optimization (ACO) in solving data mining problems for health care system [18], data clustering [19,20], classification [21,22] and travelling salesman issues [23]. Solving FS problems using metaheuristic algorithms is popular because near-optimal solutions could be obtained [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection

Almazini¹,

Ku-Mahamud²

2021

IJIES

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The performance of different mechanisms utilised to perform anomaly detection depends heavily on the group of features used. Therefore, dealing with a multi-dimensional dataset that typically contains a large number of attributes has caused problems to classification accuracy. Not all attributes in the dataset can be used in the classification process since some features may lead to low performance of classifiers. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets. Modified binary grey wolf optimization (MBGWO) is a metaheuristic algorithm that has been successfully used for FS. However, the MBGWO algorithm has a drawback in selecting sub-optimal feature sets from an original set of features. This drawback is related to the linearly decreasing value of a parameter where there is no control between the exploration and exploitation processes. This study proposed an enhanced binary grey wolf optimization (EBGWO) algorithm for FS in anomaly detection by controlling the balancing parameter. The new method focused on obtaining a value for a parameter that controlled the trade-off between exploration and exploitation. Evaluation of the proposed method was on the NLS-KDD dataset with different attack classes and compared with other benchmark algorithms, such as binary bat algorithm, binary particle swarm optimization, and four variants of grey wolf optimiser for FS. The experimental results indicated that EBGWO was superior than other algorithms, where it obtained 19 features only out of a total of 41 features with 87.46% classification accuracy. The proposed algorithm can be applied to detect anomaly in network intrusion and outliers in data that are significant but difficult to find.

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Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection

Almazini¹,

Ku-Mahamud²

2021

IJIES

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“…Conventional stochastic local search algorithms such as Tabu Search, Iterated Local Search, Simulated Annealing , and Ant colony optimazation which have been proven effective in settle this type of problems that have fickle search space with different local optima. They perform an exhaustive search via intelligent techniques (e.g., memory, and perturbation) [7][8][9]. In addition, conventional stochastic local search algorithms are inspired by natural phenomena.…”

Section: Introductionmentioning

confidence: 99%

Rule Induction with Iterated Local Search

Jabba¹

2021

IJIES

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The wide amount of data in the modern applications available on the Internet make it very complicated to deal with the knowledge behind these data. The data classification task is a useful tool that used to deal with a huge amount of data by classify these data into coherent groups. The data size decreases the performace of the classification technique, especially when contain uninformative data (i.e., irrelevant, noisy). The stochastic local search algorithm is an optimization approaches employed to find the informative data to build the classification model. These methods are used to search for the optimal patterns to construct the classification algorithm. Thus, this research introduces a stochastic local search algorithm for rule induction called Iterated-Miner (Iterated local search-based rule induction). The purpose of the algorithm is to construct classification rules from data. This classification algorithm is inspired by the concepts and principles of stochastic local search and rule induction. The performance of the proposed classifier evaluated with a well-known and state of art classification algorithms called, Ant-Miner, ACO/PSO2, PART FURIA, and ACO/GA on 10 UCI datasets. The results demonstrate that our classifier is superior compare with all classifiers respect to classification accuracy; and the model sizes by our classifier are considerably competitive with those discovered by other classifier.

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“…The search regions are recorded in the adaptive memory of the algorithm to find the regions with the best clustering solutions, which has improved during the algorithm run. ACO is the only algorithm that responds to transferring the currently recorded search regions to future iterations to be used and improved for better solutions [27], [28]. The K-adaptive MEdoid seT ACO clustering (METACOC-K) algorithm is a medoid-based algorithm that follows the same framework as ACO for clustering problems [29].…”

Section: Introductionmentioning

confidence: 99%

Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

Jabbar

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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A fundamental problem in data clustering is how to determine the correct number of clusters. The k-adaptive medoid set ant colony optimization (ACO) clustering (METACOC-K) algorithm is superior in solving clustering problems. However, METACOC-K does not guarantee in finding the best number of clusters. It assumed the number of clusters based on an adaptive parameter strategy that lacks feedback learning. This has restrained the algorithm in producing compact clusters and the optimal number of clusters. In this paper, a self-adaptive ACO clustering (S-ACOC) algorithm is proposed to produce the optimal number of clusters by incorporating a self-adaptive parameter strategy. The S-ACOC algorithm is a centroid-based algorithm that automatically adjusts the number of clusters during the algorithm run. The selection of the number of clusters is based on a construction graph that reflects the influence of a pheromone in algorithm learning. Experiments were conducted on real-world datasets to evaluate the performance of the proposed algorithm. The external evaluation metrics (purity, F-measure, and entropy) were used to compare the results of the proposed algorithm with other swarm clustering algorithms, including a genetic algorithm (GA), particle swarm optimization (PSO), and METACOC-K. Results showed that S-ACOC provides higher purity (50%) and lower entropy (40%) than GA, PSO, and METACOC-K. Experiments were also performed on several predefined clusters, and results demonstrate that the S-ACOC algorithm is superior to GA, PSO, and METACOC-K. Based on the superior performance, S-ACOC can be used to solve clustering problems in various application domains.

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Modified ACS Centroid Memory for Data Clustering

Cited by 12 publications

References 19 publications

Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection

Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection

Rule Induction with Iterated Local Search

Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters

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