International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | An Advanced Data Clustering Framework for Mining Large-Scale Data Sets |
|---|---|
| ABSTRACT | The rapid expansion of big data across diverse application domains has introduced substantial challenges in extracting useful knowledge from massive datasets. Clustering is a key unsupervised data mining technique that facilitates the identification of hidden structures by grouping similar data objects. However, conventional clustering methods such as K-Means and DBSCAN exhibit several drawbacks, including limited scalability, dependence on initial parameter settings, and degraded performance in high-dimensional and noisy data environments. This paper presents an advanced hybrid clustering framework that combines metaheuristic optimization, distributed computing, and federated learning to address these issues. The proposed methodology utilizes Variable Neighborhood Search (VNS) and Lévy Flights to enhance centroid optimization, Apache Spark for efficient parallel computation, and federated learning to maintain data privacy. Experimental validation on standard benchmark datasets demonstrates that the proposed approach delivers improved clustering accuracy, enhanced robustness, and superior scalability when compared with traditional clustering techniques. |
| AUTHOR | DR. SAURABH SHARMA Department of Computer Science and Engineering, Baderia Global Institute of Engineering & Management, Jabalpur, Madhya Pradesh, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401041 |
| pdf/41_An Advanced Data Clustering Framework for Mining Large-Scale Data Sets.pdf | |
| KEYWORDS | |
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