Functional data can be clustered by plugging estimated regression coefficients from individual curves into the k-means algorithm. Clustering results can differ depending on how the curves are fit to the data. Estimating curves using different sets of basis functions corresponds to different linear ...
Russell K H ChingYi-Shen LinPalgrave Macmillan UKJournal of the Operational Research SocietyAn extended study of the k-means algorithm for data clustering and its applications - Chen, Ching, et al. - 2004 () Citation Context ...s ease of implementation, and its relative efficiency. The ...
Partitioning clustering algorithms aim to divide the dataset into a set of non-overlapping clusters. The most popular algorithm in this category is K-means clustering. It begins by randomly selecting K initial cluster centroids and iteratively assigns each data point to the closest centroid. The cen...
One of the potential applications of our clustering algorithm(聚类算法) is in noninvasive quantative PET.The above sentence is taken from the Discussion section of a published paper. Choose the correct information component(s) from the following choices....
Chen Z, Liu W (2020) An efficient parameter adaptive support vector regression using K-means clustering and chaotic slime mould algorithm. IEEE Access 8(2):156851–156862 Article Google Scholar Toğaçar M, Ergen B, Tümen V (2022) Use of dominant activations obtained by processing OCT im...
The next building block of KDN is SDN, which enables the global network view, network programmability functions, and flexibility to manage the network. The combination of network analytics and SDN provides a foundation for the KDN paradigm. However, an ML algorithm will be the heart of KDN, ...
Hierarchical Clustering is a type of clustering algorithm which groups data points on the basis of similarity creating tree based cluster called dendrogram.
Then, a clustering algorithm is expanded where data is expressed in q-ROPFL form with unknown weight information and is explained through an illustrative example. Besides, detailed parameter analysis and comparative study are performed with the existing approaches to reveal the effectiveness of the ...
For example, the k-means algorithm based on density canopy (DCk-means) was utilized in Zhang, Zhang, and Zhang (2018) to determine the number of clusters and the position of initial values simultaneously. Moreover, projection-based clustering is one of the best conventional algorithm to ...
Centroid-based clustering calculates clusters based on a central point which may or may not be part of the data set. For centroid-based clustering, you can use the K-means clustering algorithm, which divides the data set into k clusters. Data points belong to the cluster with the nearest ...