DeepStream: Autoencoder-Based Stream Temporal Clustering Shimon Harush, Yair Meidan, Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev hshimo@post.bgu.ac.il,yairme@post.bgu.ac.il,shabtaia@bgu.ac.il ABSTRACT This paper presents DeepStream, a novel data stream temporal clustering algorithm that dynamically detects sequential and over- lapping clusters. DeepStream is tuned to classify contextual infor- mation in real time and is capable of coping with a high-dimensional feature space. DeepStream utilizes stacked autoencoders to reduce the dimensionality of unbounded data streams and for cluster rep- resentation. This method detects contextual behavior and captures nonlinear relations of the input data, giving it an advantage over existing methods that rely on PCA. We evaluated DeepStream em- pirically using four sensor and IoT datasets and compared it to five state-of-the-art stream clustering algorithms. Our evaluation shows that DeepStream outperforms all of these algorithms. CCS CONCEPTS • Computing methodologies →Machine learning algorithms; KEYWORDS Stream clustering; Autoencoder; Dimensionality reduction; Anom- aly detection ACM Reference Format: Shimon Harush, Yair Meidan, Asaf Shabtai. 2021. DeepStream: Autoencoder- Based Stream Temporal Clustering. In The 36th ACM/SIGAPP Symposium on Applied Computing (SAC ’21), March 22–26, 2021, Virtual Event, Republic of Korea. ACM, New York, NY, USA, Article 4, 4 pages. https://doi.org/10. 1145/3412841.3442083 1 INTRODUCTION Data stream clustering is a special type of clustering that has re- cently received increased attention from researchers motivated by the challenges posed by applications involving large datasets and streams of data, and the need to learn tasks in real time [1]. Several stream clustering algorithms have been proposed, most of which have two phases: an online phase, which summarizes the data into many micro-clusters, and an offline phase, in which these micro-clusters are re-clustered into a smaller number of final clus- ters [3]. However, none of these algorithms is suitable for clustering contextual sensor streams since these algorithms are designed to produce clusters that are agnostic to the order of arriving records. In contrast, the pcStream algorithm [19] is capable of capturing the temporal importance of the incoming data. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). SAC ’21, March 22–26, 2021, Virtual Event, Republic of Korea © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8104-8/21/03. https://doi.org/10.1145/3412841.3442083 We propose a stream clustering method called DeepStream, which is designed to address the above mentioned limitations of existing stream clustering algorithms. DeepStream leverages deep learning techniques to capture the nonlinear relations of input data by using the embedded representation output of a pretrained stacked autoencoder (SAE). By integrating an SAE into the tempo- ral stream clustering algorithm, our method aims to improve the ability to cluster such data streams, thereby improving the quality of the clusters and is able to deal better with high-dimensional data streams. We evaluated our proposed method using four different datasets and compared its performance with that of five existing stream clustering algorithms. The results show a significant im- provement in clustering capabilities. Summary of contributions. a) We propose a stream cluster- ing algorithm which is capable of capturing temporal contexts. As a result of integrating SAEs, our method is able to (1) capture non- linear relations betwee