A survey on nature inspired metaheuristic algorithms for partitional clustering
By Satyasai Jagannath Nanda, Ganapati Panda. Swarm and Evolutionary Computation, 2014
528 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/fd21cfb3b3a573c9ba0de366605e194b14d00d46
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Avi Ostfeld
- Dimitri P. Solomatine
- Holger R. Maier
- Laith Mohammad Abualigah
- Graeme C. Dandy
- Essam Said Hanandeh
- Ponnuthurai Nagaratnam Suganthan
- Ye Ren
In Computer Vision and Pattern Recognition
- Ye Ren
- Ponnuthurai Nagaratnam Suganthan
- Ahmet Cosar
- Tansel Dökeroğlu
- Tayfun Kucukyilmaz
- Ahamad Tajudin Abdul Khader
- Le Zhang
- Joseph R. Kasprzyk
Citations in the corpus
- Hybrid clustering analysis using improved krill herd algorithm (2018, methodology): “The only difference is that k-medoid clustering algorithm uses the document closest to the cluster center, but k-mean clustering algorithm uses average documents (vectors) that belong to the cluster to represent the cluster centroid [10, 28, 29].”
- A survey on new generation metaheuristic algorithms (2019, background): “Nanda and Panda (2014) provide a review of nature-inspired algorithms for the problem of partitional clustering.”
- Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “EAs have the ability to perform both exploration (i.e. global search) and exploitation (i.e. local search) of the fitness function, increasing the chances of finding near-optimal solutions to complex problems (Nanda and Panda, 2014).”
- Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, unknown): “Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems such as scheduling problems [1–6], data clustering [7,8], image and video processing [9–12], tuning of neural networks [13–15] and pattern recognition [16–18], etc.”
- Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “We can also investigate the benefits of ensemble methods for solving other machine learning tasks such as clustering [177], [178].”