Volume : 12, Issue : 1, JAN 2026

WEBSTUR: A TOOL FOR ANALYZING WEB USER BEHAVIOR ON PROXY SERVER USING WEB MINING

DR. JAI PRAKASH

Abstract

The growing reliance on data mining techniques across industries necessitates a comprehensive evaluation of their performance, particularly when applied to sparse datasets, a common challenge in real-world applications. This study focuses on a comparative analysis of four widely used data mining methods Apriori, FP Growth, Web Log, and Modified Web Log-evaluated across varying sparsity levels (20%, 40%, 60%, 80%, and 100%) to assess their accuracy and robustness. Employing a methodical approach, the research analyzes how these techniques adapt to the complexities of sparse environments, identifying significant variations in their performance. The Modified Web Log algorithm consistently demonstrated superior performance, achieving accuracy levels exceeding 90% across all sparsity levels, thereby proving its effectiveness in managing sparse datasets with minumal loss of information. FP Growth and Web Log showed moderate performance, maintaining accuracy between 80% and 90%, while Apriori consistently underperformed with accuracy levels below 80%, revealing its limitations in sparse conditions. These findings underscore the critical importance of algorithmic optimization for improving data processing and predictive modeling in industries such as e-commerce, healthcare, and social media, where sparsity is a prevalent issue. By highlighting the effectiveness of the Modified Web Log approach, this study contributes to the ongoing development of efficient data mining methodologies, offering valuable insights for organizations seeking to enhance decision-making and operational efficiency through improved data-driven strategies. The research also provides a foundation for further exploration into advanced algorithmic designs tailored to tackle sparsity and other challenges inherent in large-scale data environments.

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IESRJ

International Educational Scientific Research Journal

E-ISSN: 2455-295X

International Indexed Journal | Multi-Disciplinary Refereed Research Journal

ISSN: 2455-295X

Peer-Reviewed Journal - Equivalent to UGC Approved Journal

Peer-Reviewed Journal

Article No : 4

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