Automated Clustering and Knowledge Acquisition Support for Beginners
Ryota Kamoshida, Fuyuki Ishikawa · Procedia Computer Science · 2020
Although automated machine learning (AutoML) is receiving attention in the field of data science, most AutoML open source software focuses on supervised learning tasks, and little attention has been given to unsupervised learning tasks. Moreover, AutoML has a disadvantage in that it tends to deprive users of a chance to acquire knowledge about machine learning and data science because it usually works as a black box. The purpose of this study is to help inexperienced data scientists and machine learning engineers conduct clustering data analysis, which is an unsupervised learning task, while simultaneously enabling them to acquire knowledge about clustering data analysis by extending our existing AutoML software, the machine learning support system (MALSS). The MALSS helps with clustering data analysis by automatically determining the optimal number of clusters, which is one of the main purposes of clustering data analysis. Furthermore, the MALSS helps users to acquire knowledge about clustering data analysis by generating a report after automated clustering data analysis is conducted. We validated the effectiveness of our approach by using open datasets and by running an experiment on a crowdsourcing platform.