Analysis of System Requirements and Architecture for Facilitating Table-Based Data Clustering for Non-Technical Users

Abstract


 
 
 
 Clustering is one of the key techniques in unsupervised learning analysis, aimed at grouping similar data objects into clusters based on shared characteristics. The broad benefits of clustering are evident across various sectors, such as business, marketing, finance, and many others. However, the complexity of implementing clustering, especially for those without a background in statistics or programming, poses a barrier. The appropriate selection of clustering methods and accurate interpretation of results require a solid understanding of statistics. This research aims to address this issue by crafting a detailed Software Requirements Specification for a user-friendly clustering application, equipped with an intuitive interface and effective tools, based on comprehensive literature study, which finally allowing non-experts to engage in the clustering process without in-depth knowledge of statistics or programming. As such, this study endeavors to provide a practical solution for utilizing clustering without excessive technical impediments.