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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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A fuzzy co-clustering algorithm for biomedical data.

Yongli Liu¹, Shuai Wu¹, Zhizhong Liu¹

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China.

|April 27, 2017

Summary

This summary is machine-generated.

A new fuzzy co-clustering algorithm, ibFCC, improves biomedical data clustering accuracy. This information bottleneck-based method outperforms existing fuzzy clustering techniques.

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Area of Science:

Computational biology
Data mining
Machine learning

Background:

Fuzzy co-clustering enhances traditional co-clustering by incorporating membership functions for both objects and features.
Accurate clustering of biomedical data is crucial for biological insights and medical applications.

Purpose of the Study:

Introduce a novel fuzzy co-clustering algorithm, ibFCC, designed to improve the accuracy of biomedical data analysis.
Leverage information bottleneck theory within a fuzzy co-clustering framework.

Main Methods:

Developed the ibFCC algorithm, formulating an objective function with a distance measure based on information bottleneck theory.
Measured the distance between feature data points and feature cluster centroids using information bottleneck principles.
Evaluated ibFCC on five diverse biomedical datasets.

Main Results:

ibFCC demonstrated the ability to generate high-quality clusters across multiple biomedical datasets.
Comparative experiments showed ibFCC significantly outperformed established fuzzy (co-)clustering algorithms, including FCM, FCCM, RFCC, and FCCI, in terms of accuracy.

Conclusions:

The proposed ibFCC algorithm offers a superior approach for fuzzy co-clustering of biomedical data.
Information bottleneck theory integration enhances clustering performance and accuracy in complex biological datasets.