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Information bottleneck based incremental fuzzy clustering for large biomedical data.

Yongli Liu¹, Xing Wan¹

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China.

Journal of Biomedical Informatics

|June 5, 2016

Summary

This summary is machine-generated.

This study introduces two novel incremental fuzzy clustering algorithms, spFCM-IB and oFCM-IB, for classifying large biomedical literature. These methods improve accuracy over existing approaches by using information bottleneck principles.

Keywords:

Fuzzy clustering Incremental clustering Information bottleneck

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Area of Science:

Biomedical Informatics
Machine Learning
Data Mining

Background:

Classifying large biomedical literature is crucial but challenging due to data sparsity and high dimensionality.
Conventional fuzzy and incremental clustering algorithms often yield suboptimal results for such datasets.

Purpose of the Study:

To propose two novel incremental fuzzy clustering algorithms, Single-Pass fuzzy c-means with Information Bottleneck (spFCM-IB) and Online fuzzy c-means with Information Bottleneck (oFCM-IB).
To enhance the accuracy of biomedical literature classification using these new information bottleneck-based approaches.

Main Methods:

Developed spFCM-IB and oFCM-IB algorithms incorporating information bottleneck principles.
Modified conventional algorithms by assigning differential weights to centroids and objects.
Utilized mutual information loss scoring to quantify distances between centroids and objects.
Applied the algorithms to cluster biomedical text abstracts from the Medline database.

Main Results:

spFCM-IB and oFCM-IB demonstrated superior clustering performance compared to established methods like spFCM, spHFCM, oFCM, and oHFCM.
The proposed algorithms achieved higher accuracy in classifying biomedical text abstracts.
The information bottleneck approach effectively addressed challenges of data sparsity and high dimensionality.

Conclusions:

The proposed spFCM-IB and oFCM-IB algorithms offer a significant advancement in the accurate classification of large biomedical literature datasets.
Information bottleneck-based incremental fuzzy clustering is a promising direction for biomedical text analysis.
These algorithms provide a more robust solution for handling the complexities of biomedical data compared to existing methods.