COHORTNEY: Non-Parametric Clustering of Event Sequences

Abstract

There is emerging attention towards working with event sequences. In particular, clustering of event sequences is widely applicable in domains such as healthcare, marketing, and finance. Use cases include analysis of visitors to websites, hospitals, or bank transactions. Unlike traditional time series, event sequences tend to be sparse and not equally spaced in time. As a result, they exhibit different properties, which are essential to account for when developing state-of-the-art methods. The community has paid little attention to the specifics of heterogeneous event sequences. Existing research in clustering primarily focuses on classic times series data. It is unclear if proposed methods in the literature generalize well to event sequences. Here we propose COHORTNEY as a novel deep learning method for clustering heterogeneous event sequences. Our contributions include (i) a novel method using a combination of LSTM and the EM algorithm and code implementation; (ii) a comparison of this method to previous research on time series and event sequence clustering; (iii) a performance benchmark of different approaches on a new dataset from the finance industry and fourteen additional datasets. Our results show that CO-HORTNEY vastly outperforms in speed and cluster quality the state-ofthe-art algorithm for clustering event sequences.

Bank transactions embeddings help to uncover current macroeconomics

“…The goal is to construct small-dimensional representations of complex structured data [10], e.g. images [11] or sequences of events [12]. We will start from simple approach moving toward more complicated ones.…”