Spatial clustering methods in data mining: A survey. In Miller, H. and Jiawei Han (Eds.) Geographic Data Mining and Knowledge Discovery, Taylor and Francis. HAREL, D. and KOREN, Y. (2001). Clustering spatial data using random walks, In Proceedings of the 7th ACM SIGKDD, 281-286. San...
In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We...
Statistical methods OTU clustering We used Qiime (v1.9.1) and VSEARCH (v1.9.6) to classify and identify the intestinal microbiome. In brief, the process involved the following steps: [1] Unique sequences were extracted from the optimized effective sequences, and the number of repetitions of ...
Methods and Implements of Deep Clustering. Contribute to zhoushengisnoob/DeepClustering development by creating an account on GitHub.
After clustering, the sleeping nodes (SN) and waking nodes (WN) were selected, and the awake nodes will take part in the random cluster head (CH) election process which is done by using the coyote optimization algorithm. After CH selection, the stability of the link has been checked using...
In: Data clustering: algorithms and applications. Chapman and Hal, London, p 29 Google Scholar Aljalbout E, Golkov V, Siddiqui Y, Strobel M, Cremers D (2018) Clustering with deep learning: taxonomy and new methods. arXiv preprint arXiv:1801.07648 Almannaa MH, Elhenawy M, Rakha HA (...
Vayansky I, Kumar SA (2020) A review of topic modeling methods. Inf Syst 94:101582 Article MATH Google Scholar Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407 Article Google Scholar Blei DM,...
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets). - zxy-smart/Awesome-Deep-Graph-Clustering
Based on the shortcomings of the above methods, we propose a low SNR single-particle image classification method based on contrast learning, which performs well in both the simulation dataset and the real data set. The main contributions of this article are as follows: (1) In this paper, a...
This limitation has driven the adoption of advanced nonlinear techniques capable of preserving both local and global structures in the data, ensuring a more faithful representation of cellular heterogeneity. Among these methods, pairwise controlled manifold approximation projection (PaCMAP) has garnered ...