Results 11 to 20 of about 606,186 (241)

A feasible k-means kernel trick under non-Euclidean feature space

International Journal of Applied Mathematics and Computer Science, 2020
This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm.
Kłopotek Robert, Kłopotek Mieczysław, Wierzchoń Sławomir   +2 more
doaj   +1 more source

Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce [PDF]

, 2014
The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures.
Elgohary, Ahmed, Farahat, Ahmed K., Kamel, Mohamed S., Karray, Fakhri   +3 more
core   +1 more source

Pembentua Pola Desain Motif Karawo Gorontalo Menggunakan K-Means Color Quantization dan Structured Forest Edge Detecion

Jurnal Teknologi Informasi dan Ilmu Komputer, 2021
Sulaman Karawo merupakan kerajinan tangan berupa sulaman khas dari daerah Gorontalo. Motif sulaman diterapkan secara detail berdasarkan suatu pola desain tertentu. Pola desain digambarkan pada kertas dengan berbagai panduannya.
Syahrial Syahrial, Rizal Lamusu
doaj   +1 more source

A Distributed Framework for Trimmed Kernel k-Means Clustering [PDF]

, 2015
Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data.
Nikolaidis, Nikolaos, Pitas, Ioannis, Tefas, Anastasios, Tsapanos, Nikolaos   +3 more
core   +2 more sources

Ising-Based Kernel Clustering

Algorithms, 2023
Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data.
Masahito Kumagai, Kazuhiko Komatsu, Masayuki Sato, Hiroaki Kobayashi   +3 more
doaj   +1 more source

A Novel Configuration Tuning Method Based on Feature Selection for Hadoop MapReduce

IEEE Access, 2020
Configuration parameter optimization is an important means of improving the performance of the MapReduce model. The existing parameter tuning methods usually optimize all configuration parameters in MapReduce.
Jun Liu, Sule Tang, Guangxia Xu, Chuang Ma, Mingwei Lin   +4 more
doaj   +1 more source

FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Change detection is one of the most important applications of Polarimetric Synthetic Aperture Radar (PolSAR) data in monitoring urban development and supporting urban planning due to the sensibility of SAR signal to geometrical and physical properties of
L. Yousefizadeh, R. Shahhoseini, S. Homayouni   +2 more
doaj   +1 more source

A survey of kernel and spectral methods for clustering [PDF]

, 2007
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Aizerman, Aronszajn, Belkin, Bengio, Bezdek, Bishop, Burges, Camastra, Chan, Chen, Chiang, Cortes, Cristianini, Cristianini, Dhillon, Dhillon, Donath, Duda, Fiedler, Fisher, Francesco Camastra, Francesco Masulli, Gersho, Girolami, Golub, Have, Horn, Huber, Hur, Jain, Kernighan, Kluger, Kohonen, Kohonen, Krishnapuram, Krishnapuram, Kulis, Lee, Leski, Linde, Lloyd, Martinetz, Maurizio Filippone, Mercer, Müller, Ng, Ritter, Rose, Roth, Roweis, Saitoh, Schölkopf, Schölkopf, Shi, Sigillito, Sneath, Stefano Rovetta, Tax, Vapnik, von Luxburg, Ward, Weston, Wolberg, Xu, Zhang   +64 more
core   +6 more sources

Efficient Multiple Kernel k-Means Clustering With Late Fusion

IEEE Access, 2019
The recently proposed multiple-kernel clustering algorithms have demonstrated promising performance in various applications. However, most of the existing methods suffer from high computational complexity and intensive time cost.
Siwei Wang, En Zhu, Jingtao Hu, Miaomiao Li, Kaikai Zhao, Ning Hu, Xinwang Liu   +6 more
doaj   +1 more source

Identification of network modules by optimization of ratio association [PDF]

, 2007
We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association.
D. Marinazzo, Duda R. O., Garey M. R., L. Angelini, M. Pellicoro, Papoulis A., S. Boccaletti, S. Stramaglia   +7 more
core   +3 more sources