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Robust clustering by pruning outliers.

Jiang-She Zhang¹, Yiu-Wing Leung

¹Fac. of Sci., Xi'an Jiaotong Univ., China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

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This study introduces a novel pruning approach to enhance C-means clustering robustness by identifying and removing outliers. This method improves cluster accuracy in datasets with noisy points.

Area of Science:

Data Science
Machine Learning
Pattern Recognition

Background:

C-means clustering is widely used but sensitive to noisy data points.
Outliers can significantly distort cluster formation and accuracy.

Purpose of the Study:

To develop a general pruning approach for robust C-means clustering.
To enhance the resilience of C-means algorithms against outliers.

Main Methods:

A novel pruning strategy is introduced, identifying outliers based on cluster size and shape.
The approach is integrated with hard C-means, fuzzy C-means, and deterministic-annealing C-means.
New algorithms are developed by combining pruning with fuzzy and possibilistic methods.

Main Results:

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The pruning approach effectively identifies and removes outliers from datasets.
Robust versions of existing C-means algorithms were successfully created.
New fuzzy and possibilistic-based robust clustering algorithms were designed.

Conclusions:

The developed pruning approach significantly improves the robustness of C-means clustering.
The method offers a generalizable strategy for handling noisy data in clustering applications.
Numerical results confirm the effectiveness of the pruning approach in achieving robust clustering.