Optimization of K-means algorithm: Ant colony optimization

Reddy, T Namratha; Supreethi, K. P.

doi:10.1109/iccmc.2017.8282522

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In addition, the minimum intra-clustering is considered a NP-hard problem when more than three centroids are involved 9,10 . Several distance-based algorithms have been adopted to assign objects to appropriate clusters, such as identifying disease using K-means and artificial neural network (ANN) 11,12 , fuzzy Cmeans, and multi K-means 13 . Nonetheless, finding the best initial clustering centroid and avoiding becoming stuck at the local optima are the challenges of the traditional algorithms 14 .…”

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Al-Behadili

2022

Baghdad Sci.J

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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms.

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Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Al-Behadili

2022

Baghdad Sci.J

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“…Compared with traditional sorting methods based on clustering, the heuristic algorithm is a group algorithm, which improves the sorting performance by finding the global optimum. In recent years, many heuristic optimization algorithms have emerged, such as ant colony algorithm [9], artificial bee colony algorithm [10], particle swarm algorithm [11], etc. Particle swarm optimization (PSO) is a widely used algorithm, which is optimized through information sharing mechanism.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Wang

Dong

et al. 2021

IEEE Access

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Radar signal sorting is the core part of electronic support measures, which is responsible for deinterleaving the overlapping pulse sequences received by the receiver from the complex environment, separating different radiation source signals, and providing support for radiation source identification. Particle swarm optimization (PSO) is a population-based global optimization algorithm with great advantages in intelligent sorting of complex signals, which can adapt to the electromagnetic environment with complex and variable radiation source signals and high pulse stream density. However, the PSO-based sorting method is prone to premature convergence and cannot adaptively adjust particle swarm parameters and positions. In this paper, a dynamic modified chaotic PSO algorithm (DMCPSO) is proposed. Chaotic search is used to increase the diversity of particle swarm in the later iteration to avoid premature convergence and falling into local optimum. Adaptive adjustment parameters related to the particle fitness value are adopted to balance the ability of global search and local search. A new fitness function is proposed and the particle position is dynamically corrected by clustering analysis to improve the accuracy of particle position optimization and avoid the influence from the distribution of feature parameters. The simulation results show that the DMCPSO algorithm provides stable and fast performance with excellent sorting indexes in complex, variable, and dense signal environment.

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“…Peneliti lainnya dengan impelentasi yang sama yaitu dengan mengadopsi proses K-means clustering yang diproses dengan membandingkan dengan metode Ant Colony Optimization. Dari Ant Colony Optimization belum menjadi metode yang cukup optimal dikarenakan center dari perkelompokan hanya berdasarkan satu atribut saja [5]. Peneliti lainnya dengan mengadopsi anlomali data berupa data non-numerik, data tersebut diperuntukan untuk deteksi dan digunakannya metode data mining [6].…”

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Implementasi K-Means Clustering untuk Analisis Non-Numerik Dataset Spare Part Mobil

Utami,

Rahma,

Suroso

2024

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Industri otomotif merupakan salah satu sector ekonomik terbesar di dunia, dengan berbagai rantai pasok yang kompleks. Kompleksitas data otomotif yang beragai sering kali banyak mengandung atribut data non-numerik, seperti nama, jenis spare part, merk, dan atribut kualitatif lainnya. Analisis non-nurmerik dapat memberikan wawasan berharga dengan pola dan hubungan antar suku cadang. Tujuan dari penelitian ini yaitu untuk mengembangkan metode k-means clustering pada dataset spare part yang mengandung atribut non-numerik. K-means clustering adalah Teknik yang umumnya digunakan untuk analisis data numerik dan memerlukan modifikasi atau keterlibatan metode lain dalam mengatasi data non-numerik. Adapun proses yang dilibatkan yaitu proses normalisasi dengan menggunakan metode binning. Implementasi K-means Clustering pada dataset non-numerik memiliki manfaat potensial. Pertama, itu memungkinkan identifikasi kelompok dari suku cadang yang memiliki karakterisktik serupa, yang dapat digunakan untuk mengelompokkan produk serupa. Kedua yaitu untuk membantu dalam pengelolaan ketersedian dengan lebih efisien, menghindari kelebihan persedian, dan memenuhi permintaan pelanggan dengan lebih baik. Penelitian ini menghadapi tantangan dalam menentukan metrik kesamaan yang tepat untuk data non-numerik dan dalam menentukan jumlah cluster yang optimal. Namun, metodologi yang cermat dan eksperimen yang bekelanjutan, mampu mengembangkan pendekatan yang dapat digunakan dalam pengelompokan suku cadang mobil non-numerik. Hasil dari penelitian ini diperoleh sebaran data dengan menggunkan cluster K=2 dengan nilai Silhouette sebagai nilai dari sebaran data yaitu 0,925.

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Optimization of K-means algorithm: Ant colony optimization

Cited by 7 publications

References 10 publications

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Implementasi K-Means Clustering untuk Analisis Non-Numerik Dataset Spare Part Mobil

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