ItemConference Paper

A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics

Title
A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics
Creator
Mannooparambil, Joseph Mathew; Saju, Roshan Koshy; Varghese, Rohan Thomas; Reena, Melbin J
Description
Clustering is a fundamental technique in data analytics that groups data points with similar characteristics into clusters. It is crucial for uncovering hidden patterns, trends, and structures in datasets. Clustering reduces the complexity of large datasets by summarizing data into representative clusters. This simplification makes it easier to analyze and interpret data, especially when dealing with high-dimensional datasets. By identifying meaningful groups, clustering provides actionable insights that supports decision-making. For instance, businesses can make concrete decisions about product recommendations, pricing strategies, or resource allocation based on cluster analysis. The approach described in the paper offers an efficient method for combining K-means and Gaussian Mixture Model (GMM) clustering techniques. The method combines two wellknown clustering techniques, K-means and GMM, to leverage their respective strengths. K-means is known for its simplicity and efficiency, while GMM can model complex data distributions with varying covariance structures. Instead of directly integrating the results of K-means and GMM, the approach uses a simplified averaging technique to converge the cluster labels obtained independently from both methods. This suggests that the method may involve assigning weights to the cluster labels obtained from K-means and GMM and then averaging them to obtain final cluster assignments. Overall, this approach presents a promising direction for combining K-means and GMM clustering techniques, offering a streamlined integration process that simplifies the consideration of varying covariance types in GMM. The effectiveness of the method is evaluated through empirical studies and comparisons with existing clustering approaches. 2025 IEEE.
Source
Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Clustering; Evaluation Metrics; Gaussian Mixture Model; Integration; K-means; Visualization
Coverage
Mannooparambil J.M., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Saju R.K., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Varghese R.T., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Reena M.J., Christ University, Department of Computer Science and Engineering, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152118-9;
Format
online
Language
English
Type
Conference paper

Citation

Mannooparambil, Joseph Mathew; Saju, Roshan Koshy; Varghese, Rohan Thomas; Reena, Melbin J, “A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26167.

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