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Research on particle swarm optimization based clustering: a systematic review of literature and techniques
Optimization based pattern discovery has emerged as an important field in knowledge
discovery and data mining (KDD), and has been used to enhance the efficiency and …
discovery and data mining (KDD), and has been used to enhance the efficiency and …
[HTML][HTML] K-means-based nature-inspired metaheuristic algorithms for automatic data clustering problems: Recent advances and future directions
K-means clustering algorithm is a partitional clustering algorithm that has been used widely
in many applications for traditional clustering due to its simplicity and low computational …
in many applications for traditional clustering due to its simplicity and low computational …
A cloud intrusion detection systems based on dnn using backpropagation and pso on the cse-cic-ids2018 dataset
Cloud computing (CC) is becoming an essential technology worldwide. This approach
represents a revolution in data storage and collaborative services. Nevertheless, security …
represents a revolution in data storage and collaborative services. Nevertheless, security …
A review on particle swarm optimization algorithms and their applications to data clustering
Data clustering is one of the most popular techniques in data mining. It is a method of
grou** data into clusters, in which each cluster must have data of great similarity and high …
grou** data into clusters, in which each cluster must have data of great similarity and high …
Density-based particle swarm optimization algorithm for data clustering
Particle swarm optimization (PSO) algorithm is widely used in cluster analysis. However, it is
a stochastic technique that is vulnerable to premature convergence to sub-optimal clustering …
a stochastic technique that is vulnerable to premature convergence to sub-optimal clustering …
Hybrid reptile search algorithm and remora optimization algorithm for optimization tasks and data clustering
Data clustering is a complex data mining problem that clusters a massive amount of data
objects into a predefined number of clusters; in other words, it finds symmetric and …
objects into a predefined number of clusters; in other words, it finds symmetric and …
Swarm intelligence for clustering—A systematic review with new perspectives on data mining
The increase in available data has attracted the interest in clustering approaches as a way
of coherently aggregating them and identify patterns in big data. Hence, Swarm Intelligence …
of coherently aggregating them and identify patterns in big data. Hence, Swarm Intelligence …
[PDF][PDF] A review on artificial bee colony algorithms and their applications to data clustering
Data clustering is an important data mining technique being widely used in numerous
applications. It is a method of creating groups (clusters) of objects, in such a way that objects …
applications. It is a method of creating groups (clusters) of objects, in such a way that objects …
A novel hybrid PSO-K-means clustering algorithm using Gaussian estimation of distribution method and Lévy flight
Clustering is an important data analysis technique, which has been applied to many
practical scenarios. However, many partitioning based clustering algorithms are sensitive to …
practical scenarios. However, many partitioning based clustering algorithms are sensitive to …
Data clustering with modified K-means algorithm
This paper presents a data clustering approach using modified K-Means algorithm based on
the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm …
the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm …