K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data
Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
Leaf disease detection using machine learning and deep learning: Review and challenges
Identification of leaf disorder plays an important role in the economic prosperity of any
country. Many parts of a plant can be infected by a virus, fungal, bacteria, and other …
country. Many parts of a plant can be infected by a virus, fungal, bacteria, and other …
Big data for cyber physical systems in industry 4.0: a survey
With the technology development in cyber physical systems and big data, there are huge
potential to apply them to achieve personalization and improve resource efficiency in …
potential to apply them to achieve personalization and improve resource efficiency in …
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
E Schubert, PJ Rousseeuw - … Conference, SISAP 2019, Newark, NJ, USA …, 2019 - Springer
Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …
[HTML][HTML] Fast and eager k-medoids clustering: O (k) runtime improvement of the PAM, CLARA, and CLARANS algorithms
E Schubert, PJ Rousseeuw - Information Systems, 2021 - Elsevier
Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …
A review on time series aggregation methods for energy system models
Due to the high degree of intermittency of renewable energy sources (RES) and the growing
interdependences amongst formerly separated energy pathways, the modeling of adequate …
interdependences amongst formerly separated energy pathways, the modeling of adequate …
Greed is good: Algorithmic results for sparse approximation
JA Tropp - IEEE Transactions on Information theory, 2004 - ieeexplore.ieee.org
This article presents new results on using a greedy algorithm, orthogonal matching pursuit
(OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a …
(OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a …
[PDF][PDF] Refining initial points for k-means clustering.
PS Bradley, UM Fayyad - ICML, 1998 - Citeseer
Practical approaches to clustering use an iterative procedure (eg K-Means, EM) which
converges to one of numerous local minima. It is known that these iterative techniques are …
converges to one of numerous local minima. It is known that these iterative techniques are …
[图书][B] Convex analysis and global optimization
H Tuy, T Hoang, T Hoang, V Mathématicien, T Hoang… - 1998 - Springer
Optimization has been expanding in all directions at an astonishing rate during the last few
decades. New algorithmic and theoretical techniques have been developed, the diffusion …
decades. New algorithmic and theoretical techniques have been developed, the diffusion …
Cluster center initialization algorithm for K-means clustering
Performance of iterative clustering algorithms which converges to numerous local minima
depend highly on initial cluster centers. Generally initial cluster centers are selected …
depend highly on initial cluster centers. Generally initial cluster centers are selected …