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 | A New Data Clustering Approach for Data Mining in Large Data Sets |
|---|---|
| ABSTRACT | The exponential growth of big data has created significant challenges in extracting meaningful knowledge from large-scale datasets. Clustering is a fundamental unsupervised data mining technique used to discover hidden patterns and group similar data objects. However, traditional clustering algorithms such as K-Means and DBSCAN suffer from limitations related to scalability, sensitivity to initialization, and poor performance in high-dimensional and noisy environments. This paper proposes a novel hybrid data clustering framework that integrates metaheuristic optimization, distributed computing, and federated learning to overcome these challenges. The proposed approach employs Variable Neighborhood Search (VNS) and Lévy Flights for intelligent centroid optimization, Apache Spark for scalable parallel processing, and federated learning to ensure privacy preservation. Experimental evaluation on benchmark real-world datasets demonstrates that the proposed method achieves superior clustering accuracy, robustness, and scalability compared to conventional clustering techniques. |
| AUTHOR | JANHVI BAIGA, DR. SAURABH SHARMA, PROF. SAURABH VERMA Department of Computer Science and Engineering, Baderia Global Institute of Engineering & Management, Jabalpur, Madhya Pradesh, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401037 |
| pdf/37_A New Data Clustering Approach for Data Mining in Large Data Sets.pdf | |
| KEYWORDS | |
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