Mattausch, "K-means Clustering Algorithm for Mul- timedia Applications with Flexible HW/SW Co-design," Journal of Systems Architecture, vol.59, no.3, pp.155-164, 2013.F. An and H.J. Mattausch. K-means clustering algorithm for multime- dia applications with exible HW/ SW co-design....
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...
K-Means Clustering with Automatic Determination of K Using a Multi Objective Genetic Algorithm with Applications to Microarray Gene Expression DataComputer science K-means clustering with automatic determination of K using a Multiobjective Genetic Algorithm with applications to microarray gene expression data...
Through the use of a ratio limit inequality, we also prove stability of expected errors of empirical minimizers. Next, we investigate applications of the stability result. In particular, we focus on procedures that optimize an objective function, such as k-means and other clustering methods. We...
K_means: This is a clustering algorithm, which is sensitive to data structures and consists to iteratively calculate the k-distances from each class centroid to each datum [222]. The points are then assigned to the nearest cluster and the centers are re-evaluated to be the average of their...
The K-means clustering method (KMCM) and chaotic SMA (CSMA) are used in a reported SVR-based prediction system [51]. Eight separate high and low-dimensional benchmark datasets are used to measure the forecast accuracy, stability performance, and processing complexity. This technique aims to ...
According to a fundamental result of Erdös and Rényi, the structure of a random graph changes suddenly when : if and and since B Bollobás - 《Trans.amer.math.soc》 被引量: 1039发表: 1984年 A local search approximation algorithm for k-means clustering In k-means clustering we are given...
Approaches covered include support vector machines (SVM), neural networks, the genetic algorithm and k-means. The relative novelty of machine learning in this field means that many additional applications can be developed in the future, making this a rich and exciting area of research. 展开 ...
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, ...
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 ...