Performance Comparison of Two Streaming Data Clustering Algorithms

TL;DR

This paper compares two streaming data clustering algorithms, focusing on their performance and adaptations of fuzzy c-mean clustering for real-time data analysis.

Contribution

It introduces and evaluates extensions of fuzzy c-mean clustering algorithms tailored for streaming data scenarios.

Findings

Weighted fuzzy c-mean performs better in dynamic data environments

Adaptive cluster number method improves clustering accuracy

Algorithms are suitable for real-time streaming applications

Abstract

The weighted fuzzy c-mean clustering algorithm and weighted fuzzy c-mean-adaptive cluster number are extension of traditional fuzzy c-mean Algorithm to stream data clustering algorithm.