AIMC Topic: Cluster Analysis

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INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in...

Using Semantic Similarities and csbl.go for Analyzing Microarray Data.

Methods in molecular biology (Clifton, N.J.)
Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challe...

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

Behavior research methods
Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advan...

Feature selection using feature dissimilarity measure and density-based clustering: application to biological data.

Journal of biosciences
Reduction of dimensionality has emerged as a routine process in modelling complex biological systems. A large number of feature selection techniques have been reported in the literature to improve model performance in terms of accuracy and speed. In ...

[Study on UPLC Fingerprint of Corydalis bungeana].

Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials
OBJECTIVE: To establish an Ultra Performance Liquid Chromatography fingerprint of Corydalis bungeana from different habitats.

Quantitative analysis of breast echotexture patterns in automated breast ultrasound images.

Medical physics
PURPOSE: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using a...

Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery.

Bioinformatics (Oxford, England)
MOTIVATION: Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, a...

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Journal of digital imaging
Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; th...

Semi-Supervised Affinity Propagation with Soft Instance-Level Constraints.

IEEE transactions on pattern analysis and machine intelligence
Soft-constraint semi-supervised affinity propagation (SCSSAP) adds supervision to the affinity propagation (AP) clustering algorithm without strictly enforcing instance-level constraints. Constraint violations lead to an adjustment of the AP similari...