International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com SANKARA RAO LAMBURU 2 4 (July - August) 2020 07 July-August enData MiningClassificationClusteringAssociation Rule MiningPredictive AnalyticsKnowledge Discovery With the exponential growth of digital data in recent years data mining has become an essential process for extracting meaningful patterns trends and knowledge from large datasets This paper presents an analytical study of various data mining techniques and their applications across multiple domains Key methods such as classification clustering regression association rule mining and anomaly detection are examined for their effectiveness and practical implementation The study highlights how these techniques are applied in diverse fields including healthcare finance education marketing and cybersecurity to support datadriven decisionmaking Furthermore the paper discusses challenges such as data quality scalability privacy and model interpretability that affect mining performance Comparative analysis reveals that hybrid and ensemblebased approaches often enhance accuracy and efficiency in complex datasets Overall this study emphasizes the growing significance of data mining as a core component of modern analytics and its role in transforming raw data into valuable insights for intelligent decision support systems Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2020 All Rights Reserved