Performance Analysis of Supervised and Unsupervised Learning Models in Real-Time Data Classification
Keywords:
Real-time classification, supervised learning, unsupervised learning, machine learning, data stream, performance analysisAbstract
Real-time data classification is crucial across domains like healthcare, finance, cybersecurity, and autonomous systems. Supervised and unsupervised learning models have emerged as core components in developing adaptive, intelligent, and scalable systems. This paper provides a comparative analysis of these two paradigms using multiple evaluation metrics and discusses their practical relevance to real-time data streams. Experimental simulations on benchmark datasets reveal that supervised models yield higher accuracy, whereas unsupervised models are advantageous in unlabeled or dynamic scenarios
References
Gama, J., et al. (2010). Knowledge discovery from data streams. Chapman & Hall/CRC.
Devalla, S. (2020). Performance benchmarking of Java garbage collectors in containerized microservices. Journal of Scientific and Engineering Research, 7(6), 326–334.
Aggarwal, C. (2015). Data mining: The textbook. Springer.
Domingos, P., & Hulten, G. (2001). Mining high-speed data streams. KDD, 71–80.
Devalla, S. (2020). Beyond Redux: State management and developer productivity in enterprise SPAs. European Journal of Advances in Engineering and Technology, 7(4), 70–78.
Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine learning. NIPS, 409–415.
Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. KDD, 97–106.
Devalla, S. (2019). Unveiling the enterprise value of PaaS: A comparative study of productivity, scalability, and cost efficiency against SaaS and IaaS. European Journal of Advances in Engineering and Technology, 6(2), 120–126.
Lazarevic, A., et al. (2003). A comparative study of anomaly detection schemes. SIAM International Conference on Data Mining.
Kleinberg, J. (2002). Bursty and hierarchical structure in streams. KDD, 91–101.
Devalla, S. (2019). Adaptive security frameworks for Java EE 8 and JSF: Automating threat detection and mitigation in enterprise web applications. Journal of Scientific and Engineering Research, 6(10), 326–334.
Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools. Morgan Kaufmann.
Bifet, A., & Kirkby, R. (2009). Data stream mining: A practical approach. University of Waikato.
Devalla, S. (2018). Performance benchmarking of RESTful and SOAP APIs in enterprise IoT control systems. Journal of Scientific and Engineering Research, 5(11), 376–390.
Chen, Y., et al. (2006). Real-time classification of massive data streams. IEEE Internet Computing, 10(6), 28–35.
