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Related Experiment Video

Updated: Jul 22, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using

Robinson Joel M¹, Manikandan G¹, Bhuvaneswari G²

¹Information Technology, Kings Engineering College, Sriperumbudur, India.

Computer Methods in Biomechanics and Biomedical Engineering

|July 24, 2023

Summary

This study presents a new incremental data clustering model using the Namib Beetle Mayfly Algorithm (NBMA). The approach enhances feature selection and clustering accuracy for better data analysis.

Keywords:

Data clustering Mayfly Algorithm (MA)Namib beetle optimization (NBO)deep maxout network (DMN)incremental data clustering

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Area of Science:

Data Science
Machine Learning
Artificial Intelligence

Background:

Incremental data clustering is crucial for analyzing evolving datasets.
Existing methods face challenges in feature selection and accurate weight updates.
Optimizing clustering algorithms requires efficient feature selection and adaptive weight mechanisms.

Purpose of the Study:

To introduce an effective incremental data clustering model.
To enhance feature selection and weight optimization using a novel algorithm.
To improve the performance of incremental clustering on dynamic data.

Main Methods:

Feature selection using Support Vector Machine Recursive Feature Elimination (SVM-RFE).
Weight parameter optimization and update via Gradient Namib Beetle Mayfly Algorithm (NBMA).
Clustering using entropy weighted power k-means, with centroid updates via Deep Maxout Network (DMN).

Main Results:

The proposed NBMA model demonstrates superior performance in incremental data clustering.
Optimized feature selection and weight updates lead to improved clustering accuracy.
The integration of DMN for centroid updates enhances model adaptability.

Conclusions:

The developed Entropy weighted-Gradient NBMA model offers an efficacious solution for incremental data clustering.
The hybrid approach combining SVM-RFE, NBMA, and DMN provides robust and accurate clustering.
This research contributes a significant advancement in handling dynamic and large-scale datasets.