Exploring Active Outlier Detection in Big Data and Its Applications Across Diverse Domains

Abstract

Outalier detection is an important component of data mining, especially important in the age of Big data, which is characterized by broad, complex and rapidly changing datasets in various fields including finance, healthcare, monitoring and cyber security. This research examines the active outer identity in large data settings and its practical appropriateness in many areas. The main goal is to investigate the current functioning, understand their shortcomings, and to assess the prevention and efficacy of the outer identity algorithm sophisticated in addressing the complexities of real -world data. A qualitative research technique is used, the papers of scholars published from 2018 to 2024 are enriched by secondary data collected from technical reports and empirical studies. The study conducts a comprehensive examination of several algorithms, including adaptive and hybrid models through material analysis. Conclusions suggest that sophisticated, adapted models’ cross traditional techniques in scalability, accuracy and relevance. Conclusions highlight the increasing importance of active outlair detections as a strategic means in making data-powered decision making, providing effective solutions to identify discrepancy in many areas.