Acquiring Knowledge by Performing Classification and Clustering of Datasets using WEKA: Intelligence through MLP, RF, DT, and RepTree

In the ever-advancing domains of Computer Science and Artificial Intelligence, the pursuit of knowledge acquisition through data analysis stands as an imperative pillar. The research paper titled “Acquiring Knowledge by Performing Classification and Clustering of Datasets using WEKA” represents a seminal contribution in this endeavour, encapsulating cutting-edge techniques that reverberate across the spectrum of machine learning and data mining. This research unfolds with a rigorous exploration of the WEKA software, a quintessential tool in the practitioner's arsenal, renowned for its efficacy in handling diverse datasets. Through systematic classification and clustering methodologies, this study leverages the prowess of WEKA to discern hidden patterns, structures, and knowledge encapsulated within the data. In the intricate tapestry of Computer Science and AI, the paper elucidates the intricacies of classification, a fundamental tenet of machine learning, wherein data instances are categorized into predefined classes. Simultaneously, the study delves into clustering, a quintessential technique for unsupervised learning, wherein data points are grouped based on inherent similarities. These techniques are imbued with profound implications for data mining, a discipline that underpins decisionmaking and knowledge extraction. The practical implications of this research reverberate across diverse applications in Computer Science and AI, encompassing data-driven domains such as NLP, Computer Vision, and Robotics. The discernment of patterns within data yields actionable insights, shaping the evolution of intelligent systems and augmenting the capabilities of autonomous entities.