AutoML classifier clustering procedure

International Journal of Intelligent SystemsVolume 37, Issue 7 p. 4214-4232 RESEARCH ARTICLE AutoML classifier clustering procedure Oded Koren, Oded Koren orcid.org/0000-0002-7573-2455 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this authorCarina A. Hallin, Corresponding Author Carina A. Hallin caah@itu.dk orcid.org/0000-0002-7656-043X Business IT Department, Collective Intelligence Research Group, IT University of Copenhagen, Copenhagen, Denmark Center for Collective Intelligence, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA Correspondence Carina A. Hallin, Business IT Department, Collective Intelligence Research Group, IT University of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen, Denmark. Email: caah@itu.dkSearch for more papers by this authorMichal Koren, Michal Koren orcid.org/0000-0003-2501-3361 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this authorAmir A. Issa, Amir A. Issa orcid.org/0000-0002-6424-2899 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this author Oded Koren, Oded Koren orcid.org/0000-0002-7573-2455 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this authorCarina A. Hallin, Corresponding Author Carina A. Hallin caah@itu.dk orcid.org/0000-0002-7656-043X Business IT Department, Collective Intelligence Research Group, IT University of Copenhagen, Copenhagen, Denmark Center for Collective Intelligence, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA Correspondence Carina A. Hallin, Business IT Department, Collective Intelligence Research Group, IT University of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen, Denmark. Email: caah@itu.dkSearch for more papers by this authorMichal Koren, Michal Koren orcid.org/0000-0003-2501-3361 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this authorAmir A. Issa, Amir A. Issa orcid.org/0000-0002-6424-2899 School of Industrial Engineering and Management, Shenkar—Engineering. Design. Art, Ramat Gan, IsraelSearch for more papers by this author First published: 23 October 2021 https://doi.org/10.1002/int.22718Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Recommendation systems are one of the main applications of machine learning (ML) used across different industries. This paper presents a new automated machine learning (AutoML) method of providing recommendations by processing data sets using ML algorithms, targeting, and offering cluster recommendations for new observations and as a new decision support method. The AutoML conducts a complete procedure and includes analysis and division of data into an efficient number of clusters. We apply the k-means, using the elbow method to calculate costs per cluster, followed by analyzing the allocation of the data into the clusters, thus providing a method for prediction and for allocating new observations to the relevant clusters (knn). This study includes two experiments using the complete AutoML procedure conducted on a data set, with more than two million records and dozens of attributes. This was done to demonstrate how the AutoML method can be implemented and successfully run with a high-capacity analysis procedure. The motivation was to analyze, examine, assign, and integrate new observations into existing clusters that have been defined. The results showed that the AutoML method provided efficient recommendations for new observations with an accuracy rate of 99.99%. Hence, the AutoML procedure can offer a full system for any organization to efficiently split existing data into clusters, assign to clusters, and predict the cluster allocation of new observations. The significant contribution of this study is a simple method that can achieve fast and high accuracy clustering for ongoing (new) classified data acquired by an organization. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Volume37, Issue7July 2022Pages 4214-4232 RelatedInformation