Design and Implementation of a Data Mining Pattern Discovery System
Data Science — 2025, Undergraduate
association rulesweb applicationpattern discoverydata mining
This study designed and implemented a data mining pattern discovery system that runs association, clustering, and outlier analyses on uploaded datasets and explains discovered patterns. Users select algorithms and inspect frequent itemsets and cluster profiles. Adopting a client-server architecture and a survey-based usability evaluation, the system was built using Python, Flask, and Scikit-learn. The findings showed that the system made pattern discovery accessible to students and analysts, while achieving a high System Usability Scale score. The study recommends use in academic and market-analysis settings.
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