We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1142/S0129065708001518 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Mitochondria-Related Signature in Diffuse Large B-Cell Lymphoma: Prognosis, Immune and Therapeutic Features.
Cancer Med
January 2025
Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Background: Distinctive heterogeneity characterizes diffuse large B-cell lymphoma (DLBCL), one of the most frequent types of non-Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL.
Objective: The purposes of this study were to identify the prognostic mitochondria-related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms.
Study of spectral overlap and heterogeneity in agriculture based on soft classification techniques.
MethodsX
June 2025
Department of Biological and Pharmaceutical Environmental Sciences and Technologies, University of Campania "L. Vanvitelli", Via Antonio Vivaldi, 43, Caserta 81100, CE, Italy.
This study explores the application of fuzzy soft classification techniques combined with vegetation indices to address spectral overlap and heterogeneity in agricultural image processing. The methodology focuses on the integration of three key vegetation indices: Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Modified Chlorophyll Absorption in Reflectance Index (MCARI), with Modified Possibilistic C-Means (MPCM) clustering. The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership.
View Article and Find Full Text PDFDraft genome dataset of strain R-35 isolated from tidal pool sediments.
Data Brief
February 2025
Applied Microbial and Health Biotechnology Institute, Cape Peninsula University of Technology, PO Box 1906, Bellville, Cape Town, 7530, South Africa.
The marine isolate, strain R-35, was isolated from marine sediments collected from the Glencairn Tidal Pool, Table Mountain National Park, Cape Town, South Africa. The genomic DNA was sequenced using the Ion Torrent GeneStudio™ S5 platform, and the assembly was performed using the SPAdes assembler on the Centre for High Performance Computing (CHPC) Lengau Cluster located at the CSIR, Rosebank, South Africa. The draft genome assembly consisted of 722 contigs totaling 7,625,174 base pairs and a G+C% content of 72.
View Article and Find Full Text PDFRobust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.
Biol Methods Protoc
January 2025
Department of Physics, George Washington University, Washington, DC 20052, United States.
A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference.
View Article and Find Full Text PDFT cell receptor signaling pathway subgroups and construction of a novel prognostic model in osteosarcoma.
Heliyon
January 2025
Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background: T cell receptor (TCR) signaling pathway is closely related to tumor progress and immunotherapy. This study aimed to explore the clinical significance, prognosis, immune infiltration and chemotherapy sensitivity of TCR in osteosarcoma (OS).
Material And Methods: OS data were obtained from TARGET and GEO database.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!