Analyzing and Clustering Pareto-Optimal Objects in Data Streams

M. Endres, J. Kastner, L. Rudenko

erschienen 2018 In M. Sayed-Mouchaweh (Eds.): Large-scale Learning from Data Streams in Evolving Environments, Springer


Stream data analysis is a high relevant topic in various academic and business fields. Users want to analyze data streams to extract information in order to learn from this ever-growing amount of data. Although many approaches exist for effective processing of data streams, learning from streams requires new algorithms and methods to be able to learn under the evolving and unbounded data. In this chapter we focus on the task of preference-based stream processing and clustering to analyze data streams. We show that this method is a real alternative to the state-of-the-art approaches.