Semantic content-based image retrieval: A comprehensive study
The complexity of multimedia contents is significantly increasing in the current digital world.
This yields an exigent demand for develo** highly effective retrieval systems to satisfy …
This yields an exigent demand for develo** highly effective retrieval systems to satisfy …
Minimum spanning tree hierarchical clustering algorithm: a new Pythagorean fuzzy similarity measure for the analysis of functional brain networks
Clustering structures are one of the most important aspects of complex networks. Minimum
spanning tree (MST), the tree that connects all vertices with minimum total weight, can be …
spanning tree (MST), the tree that connects all vertices with minimum total weight, can be …
Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization
X Zhang, Q Lin, W Mao, S Liu, Z Dou, G Liu - Applied Soft Computing, 2021 - Elsevier
Abstract Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithm are
two popular swarm intelligence optimization algorithms and these two algorithms have their …
two popular swarm intelligence optimization algorithms and these two algorithms have their …
A fast O (NlgN) time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets
Clustering has been widely employed for extracting intrinsic groups because of its low
reliance on domain knowledge. Though several clustering techniques have been developed …
reliance on domain knowledge. Though several clustering techniques have been developed …
An efficient k-means clustering filtering algorithm using density based initial cluster centers
KM Kumar, ARM Reddy - Information Sciences, 2017 - Elsevier
Abstract k-means is a preeminent partitional based clustering method that finds k clusters
from the given dataset by computing distances from each point to k cluster centers iteratively …
from the given dataset by computing distances from each point to k cluster centers iteratively …
A fast spectral clustering technique using MST based proximity graph for diversified datasets
Spectral clustering is a popular unsupervised learning technique used for exploratory
analysis of complex datasets. In spectral clustering, the efficient construction of a sparse …
analysis of complex datasets. In spectral clustering, the efficient construction of a sparse …
Efficiency of random swap clustering
P Fränti - Journal of big data, 2018 - Springer
Random swap algorithm aims at solving clustering by a sequence of prototype swaps, and
by fine-tuning their exact location by k-means. This randomized search strategy is simple to …
by fine-tuning their exact location by k-means. This randomized search strategy is simple to …
A clustering ensemble: Two-level-refined co-association matrix with path-based transformation
The aim of clustering ensemble is to combine multiple base partitions into a robust, stable
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …
Market basket analysis: Complementing association rules with minimum spanning trees
This study proposes a methodology for market basket analysis based on minimum spanning
trees, which complements the search for significant association rules among the vast set of …
trees, which complements the search for significant association rules among the vast set of …
MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters
A Şenol - Neural Computing and Applications, 2023 - Springer
Clustering is a technique for statistical data analysis and is widely used in many areas
where class labels are not available. Major problems related to clustering algorithms are …
where class labels are not available. Major problems related to clustering algorithms are …