A Hybrid Algorithm Based Robust Big Data Clustering for Solving Unhealthy Initialization, Dynamic Centroid Selection and Empty clustering Problems with Analysis

TL;DR

This paper introduces EG K-MEANS, a hybrid algorithm that enhances big data clustering by addressing initialization, centroid selection, and empty cluster issues, leading to more accurate and reliable results.

Contribution

The paper presents a novel hybrid algorithm, EG K-MEANS, that improves traditional K-MEANS clustering for big data by solving key problems affecting cluster quality.

Findings

EG K-MEANS effectively prevents unhealthy initialization.

The algorithm dynamically selects centroids for better clustering.

It reduces empty cluster occurrences in big data scenarios.

Abstract

Big Data is a massive volume of both structured and unstructured data that is too large and it also difficult to process using traditional techniques. Clustering algorithms have developed as a powerful learning tool that can exactly analyze the volume of data that produced by modern applications. Clustering in data mining is the grouping of a particular set of objects based on their characteristics. The main aim of clustering is to classified data into clusters such that objects are grouped in the same clusters when they are corresponding according to similarities and features mainly. Till now, K-MEANS is the best utilized calculation connected in a wide scope of zones to recognize gatherings where cluster separations are a lot than between gathering separations. Our developed algorithm works with K-MEANS for high quality clustering during clustering from big data. Our proposed algorithm EG K-MEANS : Extended Generation K-MEANS solves mainly three issues of K-MEANS: unhealthy initialization, dynamic centroid selection and empty clustering. It ensures the best way of preventing unhealthy initialization, dynamic centroid selection and empty clustering problems for getting high quality clustering.