AIMC Topic: Cluster Analysis

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Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

Journal of general internal medicine
BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.

A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.

Journal of neuroscience methods
BACKGROUND: The Allen Mouse Brain Atlas allows study of the brain's molecular anatomy at cellular scale, for thousands genes. To fully leverage this resource, one must register histological images of brain tissue - a task made challenging by the brai...

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

Computational biology and chemistry
BACKGROUND: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target inte...

Neurofuzzy c-Means Network-Based SCARA Robot for Head Gimbal Assembly (HGA) Circuit Inspection.

Computational intelligence and neuroscience
Decision and control of SCARA robot in HGA (head gimbal assembly) inspection line is a very challenge issue in hard disk drive (HDD) manufacturing. The HGA circuit called slider FOS is a part of HDD which is used for reading and writing data inside t...

Reply to 'Dissimilarity measures affected by richness differences yield biased delimitations of biogeographic realms'.

Nature communications
Recently, we classified the oceans into 30 biogeographic realms based on species' endemicity. Castro-Insua et al. criticize the choices of dissimilarity coefficients and clustering approaches used in our paper, and reanalyse the data using alternativ...

A density-based competitive data stream clustering network with self-adaptive distance metric.

Neural networks : the official journal of the International Neural Network Society
Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data ...

Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.

IEEE transactions on medical imaging
Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of...

A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve.

Chemosphere
The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growi...

Laplacian regularized low-rank representation for cancer samples clustering.

Computational biology and chemistry
Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is of great importance for cancer treatment. In this paper, in order to improve the accuracy of cancer...

CIBS: A biomedical text summarizer using topic-based sentence clustering.

Journal of biomedical informatics
Automatic text summarizers can reduce the time required to read lengthy text documents by extracting the most important parts. Multi-document summarizers should produce a summary that covers the main topics of multiple related input texts to diminish...