Clustering using firefly algorithm: Performance study

Senthilnath, J.; Omkar, S. N.; Mani, V.

doi:10.1016/j.swevo.2011.06.003

Cited by 434 publications

(162 citation statements)

References 18 publications

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“…In this study, we adapt the standard FA which has been implemented in data clustering (Senthilnath et al, 2011) and present two variants of FA that are to be used in text clustering. The variants include the WFA (Mohammed et al, 2014) and WFA II which enhance the exploitation of WFA that leads to better clustering.…”

Section: Related Workmentioning

confidence: 99%

“…4. When the standard FA is applied in clustering (Senthilnath et al, 2011;Rui et al, 2012;Banati and Bajaj, 2013), the number of fireflies are pre-defined and each firefly carries one random solution. This solution is the K number of clusters; hence we must pre-determine the value of k. However, such an approach is not suitable when we do not have any knowledge about the dataset.…”

Section: Standard Firefly Algorithmmentioning

confidence: 99%

“…Existing work on clustering numerical data using FA has been demonstrated in (Senthilnath et al, 2011;Banati and Bajaj, 2013). Nevertheless, the FA has not been tested on text data.…”

Section: Firefly Algorithm For Text Clusteringmentioning

confidence: 99%

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Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization. This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering. We present two variants of FA; Weight-based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFA II ). The difference between the two algorithms is that the WFA II, includes a more restricted condition in determining members of a cluster. The proposed FA methods are later evaluated using the 20Newsgroups dataset. Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFA II outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved.

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Section: Related Workmentioning

confidence: 99%

Section: Standard Firefly Algorithmmentioning

confidence: 99%

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Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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“…Accordingly, in this paper, we have included multiple kernel-based clustering algorithm with firefly optimization. This paper considers the firefly algorithm [3] instead of cuckoo search algorithm which is utilized in [13]. At first, the data is pre-processed by removing the missing variables.…”

Section: Introductionmentioning

confidence: 99%

MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

Chander¹,

Vijaya²

2016

International Journal of Advanced Research in Computer and Comm

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Clustering find various application in the variety fields like, telecommunication, medical image processing, bioinformatics and so on. This high demand poses a challenge to the researchers to develop an effective and efficient clustering algorithm for grouping the data objects. Accordingly, literature presents various algorithms for data clustering using partitional-based approaches. This paper presents a new clustering algorithm, namely, MKF-Firefly which is developed by combining the multiple kernel-based objective function and firefly algorithm. The firefly algorithm finds the optimal cluster centroids using the multiple kernel-based objective function. The centroids obtained from the firefly algorithm are then utilized for clustering process. The proposed clustering process is evaluated using Rand coefficient, Jaccard coefficient and Clustering Accuracy on the two different datasets like iris and wine. The proposed MKF-firefly achieved the clustering accuracy of 97% on the iris dataset.

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“…Clustering can be perceived as combinatorial optimization problem -which is known to be NP-hard [5]. It is the reason why diverse heuristic approaches have been already used to tackle it [6], [7]. As a point of reference classic K-means [8] algorithm can be named.…”

mentioning

confidence: 99%

Data Clustering with Grasshopper Optimization Algorithm

Łukasik¹,

Kowalski²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the possibility of using novel nature-inspired technique -Grasshopper Optimization Algorithm (GOA) -to generate accurate data clusterings. As a quality measure of produced solutions internal clustering validation measure of Calinski-Harabasz index is being employed. This paper provides description of proposed algorithm along with its experimental evaluation for a set of benchmark instances. Over a course of our study it was established that clustering based on GOA is characterized by high accuracy -when compared with standard K-means procedure.

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Clustering using firefly algorithm: Performance study

Cited by 434 publications

References 18 publications

Document Clustering Based on Firefly Algorithm

Document Clustering Based on Firefly Algorithm

MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

Data Clustering with Grasshopper Optimization Algorithm

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