Swarm Intelligence-Based Clustering Algorithms: A Survey

2016 ◽  
pp. 303-341 ◽  
Author(s):  
Tülin İnkaya ◽  
Sinan Kayalıgil ◽  
Nur Evin Özdemirel
Keyword(s):  
Swarm Intelligence ◽  
Clustering Algorithms

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

scholarly journals Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137560-137569 ◽  
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

Psoriasis Lesion Detection Using Hybrid Seeker Optimization Based Image Clustering

2021 ◽  
Vol 17 ◽  
Author(s):  
Manoranjan Dash ◽  
Narendra Digambar Londhe ◽  
Subhojit Ghosh ◽  
Ritesh Raj ◽  
Rajendra Sonawane
Keyword(s):  
Swarm Intelligence ◽  
Clustering Algorithms ◽  
Optimization Technique ◽  
Evolutionary Optimization ◽  
Superior Performance ◽  
Initial Population ◽  
Psoriatic Lesion ◽  
Lesion Segmentation ◽  
Evolutionary Optimization Technique ◽  
Psoriasis Lesion

Background: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a pre-requisite for quantifying the severity of this disease. However, segmentation of psoriatic lesion cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. Objective: An alternative method for psoriatic lesion segmentation with the objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique which possesses a higher probability of global convergence for psoriasis lesion segmentation. Method: A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms; namely Artificial Bee Colony and Seeker Optimization algorithm has been proposed. The initial population for the hybrid technique is obtained from the two conventional local-based approaches i.e. Fuzzy C-means and K-means clustering algorithms. Results: The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with Jaccard Index of 0.91 from 720 psoriasis images. Conclusion: The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

Swarm Intelligence in Text Document Clustering

2010 ◽  
pp. 165-180 ◽  
Author(s):  
Xiaohui Cui
Keyword(s):  
Swarm Intelligence ◽  
Clustering Analysis ◽  
Information Society ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
High Quality ◽  
Fish Schools ◽  
Text Document ◽  
Self Organized ◽  
Swarm Algorithms

In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. The major challenge of today’s information society is being overwhelmed with information on any topic they are searching for. Fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the overwhelmed information. The swarm intelligence clustering algorithms use stochastic and heuristic principles discovered from observing bird flocks, fish schools, and ant food forage. Compared to the traditional clustering algorithms, the swarm algorithms are usually flexible, robust, decentralized, and self-organized. These characters make the swarm algorithms suitable for solving complex problems, such as document clustering.

scholarly journals Correction to “Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data”

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 110251-110251
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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