Results 51 to 60 of about 843,626 (294)
Efficient Computation of Fitness Function for Evolutionary Clustering
Mendel, 2019 Evolutionary algorithms (EAs) are random search heuristics which can solve various optimization problems. There are plenty of papers describing different approaches developed to apply evolutionary algorithms to the clustering problem, although none of ...
Sergey Muravyov +3 more
doaj +1 more source
Co-Clustering Ensemble Based on Bilateral K-Means Algorithm
IEEE Access, 2020 Clustering ensemble technique has been shown to be effective in improving the accuracy and stability of single clustering algorithms. With the development of information technology, the amount of data, such as image, text and video, has increased rapidly.
Hui Yang +3 more
doaj +1 more source
Cell wall target fragment discovery using a low‐cost, minimal fragment library
FEBS Letters, EarlyView.LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
Molecular Oncology, EarlyView.Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice +16 more
wiley +1 more source
Self-adaptive GA, quantitative semantic similarity measures and ontology-based text clustering [PDF]
, 2008 As the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms which do not co-occur literally are ignored.
Li, Chenghua +3 more
core +1 more source
Molecular Oncology, EarlyView.We show that the majority of the 18 analyzed recurrent cancer‐associated ERBB4 mutations are transforming. The most potent mutations are activating, co‐operate with other ERBB receptors, and are sensitive to pan‐ERBB inhibitors. Activating ERBB4 mutations also promote therapy resistance in EGFR‐mutant lung cancer.
Veera K. Ojala +15 more
wiley +1 more source
Vector quantization using k‐means clustering neural network
Electronics Letters, 2023 Vector Quantization (VQ) is a clustering problem in the fields of signal processing, source coding, information theory etc. Taking advantage of recent advances in the field of deep neural networks, this paper investigates the performance between VQ ...
Sio‐Kei Im, Ka‐Hou Chan
doaj +1 more source
Metode clustering dengan pendekatan program linier [PDF]
, 2000 Clustering Method with Linear Programming Approach is used to solve The Clustering Problem. Clustering problem is presented in a network which covers node (representing town) and arch (representing cost should be spent by the investor). By using Labeling
Pramudiyanto. , Anang Setyo
core
Crowdsourced correlation clustering with relative distance comparisons
, 2017 Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information.
Ukkonen, Antti
core +1 more source
CUR Decompositions, Similarity Matrices, and Subspace Clustering [PDF]
, 2018 A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces $\mathscr{U ...
Aldroubi, Akram +3 more
core +3 more sources