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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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Updated: Jun 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A framework for feature selection in clustering.

Daniela M Witten, Robert Tibshirani

    Journal of the American Statistical Association
    |September 3, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new sparse clustering framework that identifies relevant features for grouping data. This method enhances cluster accuracy by adaptively selecting a subset of features, outperforming traditional methods on simulated and genomic datasets.

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    Area of Science:

    • Machine Learning
    • Bioinformatics
    • Statistics

    Background:

    • Clustering high-dimensional data often faces challenges due to irrelevant features.
    • Traditional clustering methods may fail to identify true clusters when only a subset of features is informative.

    Purpose of the Study:

    • To develop a novel framework for sparse clustering that adaptively selects informative features.
    • To improve the accuracy of clustering by focusing on a relevant subset of features.

    Main Methods:

    • Proposed a novel framework for sparse clustering using a lasso-type penalty.
    • Developed sparse K-means and sparse hierarchical clustering methods.
    • A single criterion optimized both feature selection and cluster formation.

    Main Results:

    • The proposed sparse clustering methods effectively identified underlying clusters using a subset of features.
    • Demonstrated the framework's utility on simulated data.
    • Validated the approach on real-world genomic datasets.

    Conclusions:

    • Sparse clustering offers a powerful approach for analyzing high-dimensional data.
    • The developed methods provide accurate and efficient solutions for feature selection and clustering.
    • This framework has significant implications for bioinformatics and other fields dealing with large feature sets.