Authors: Dennis Assenmacher, Heike Trautmann, Matthias Carnein
Tags: 2017, conceptual modeling
This paper proposes a new stream clustering algorithm for text streams. The algorithm combines concepts from stream clustering and text analysis in order to incrementally maintain a number of text droplets that represent topics within the stream. Our algorithm adapts to changes of topic over time and can handle noise and outliers gracefully by decaying the importance of irrelevant clusters. We demonstrate the performance of our approach by using more than one million real-world texts from the video streaming platform Twitch.tv.Read the full paper here: https://link.springer.com/chapter/10.1007/978-3-319-70625-2_8
