An entropy-based initialization method of K-means clustering on the optimal number of clusters
Clustering is an unsupervised learning approach used to group similar features using
specific mathematical criteria. This mathematical criterion is known as the objective function …
specific mathematical criteria. This mathematical criterion is known as the objective function …
Clustering of web search results based on the cuckoo search algorithm and balanced Bayesian information criterion
C Cobos, H Muñoz-Collazos, R Urbano-Muñoz… - Information …, 2014 - Elsevier
The clustering of web search results–or web document clustering–has become a very
interesting research area among academic and scientific communities involved in …
interesting research area among academic and scientific communities involved in …
ECKM: An improved K-means clustering based on computational geometry
A modified version of traditional k-means clustering algorithm applying computational
geometry for initialization of cluster centers has been presented in this paper. It is well …
geometry for initialization of cluster centers has been presented in this paper. It is well …
Seed selection algorithm through K-means on optimal number of clusters
Clustering is one of the important unsupervised learning in data mining to group the similar
features. The growing point of the cluster is known as a seed. To select the appropriate seed …
features. The growing point of the cluster is known as a seed. To select the appropriate seed …
A hybrid MapReduce-based k-means clustering using genetic algorithm for distributed datasets
Clustering a large volume of data in a distributed environment is a challenging issue. Data
stored across multiple machines are huge in size, and solution space is large. Genetic …
stored across multiple machines are huge in size, and solution space is large. Genetic …
Machine learning and GIS approach for electrical load assessment to increase distribution networks resilience
A Bosisio, M Moncecchi, A Morotti, M Merlo - Energies, 2021 - mdpi.com
Currently, distribution system operators (DSOs) are asked to operate distribution grids,
managing the rise of the distributed generators (DGs), the rise of the load correlated to heat …
managing the rise of the distributed generators (DGs), the rise of the load correlated to heat …
Determination of the appropriate parameters for K‐means clustering using selection of region clusters based on density DBSCAN (SRCD‐DBSCAN)
O Limwattanapibool, S Arch‐int - Expert Systems, 2017 - Wiley Online Library
K‐means clustering can be highly accurate when the number of clusters and the initial
cluster centre are appropriate. An inappropriate determination of the number of clusters or …
cluster centre are appropriate. An inappropriate determination of the number of clusters or …
Efficient k-means based clustering scheme for mobile networks cell sites management
Telecommunication network infrastructures in Africa and the Middle East regions, are
deployed and operated in challenging environments that are highly scattered particularly in …
deployed and operated in challenging environments that are highly scattered particularly in …
Auto-splitting D* lite path planning for large disaster area
S Heo, J Chen, Y Liao, H Lee - Intelligent Service Robotics, 2022 - Springer
This research introduces a new path planning method for rescue robots in a dynamic and
partially known area when the robots are performing tasks in a large area. The path …
partially known area when the robots are performing tasks in a large area. The path …
A study on initial centroids selection for partitional clustering algorithms
M Motwani, N Arora, A Gupta - Software Engineering: Proceedings of CSI …, 2019 - Springer
Data mining tools and techniques allow an organization to make creative decisions and
subsequently do proper planning. Clustering is used to determine the objects that are similar …
subsequently do proper planning. Clustering is used to determine the objects that are similar …