Expectation-Maximization Binary Clustering for Behavioural Annotation

TL;DR

This paper introduces a constrained Expectation-Maximization clustering algorithm that partitions data into high and low values, enhancing interpretability, especially for bimodal distributions and uncertain data, demonstrated on movement trajectory annotation.

Contribution

The paper presents a novel EM-based binary clustering algorithm that incorporates data reliability and is applicable to behavioral annotation and general multivariate data segmentation.

Findings

Effective in behavioral annotation of movement trajectories

Handles data uncertainty by weighting input reliability

Suitable for bimodal and binary-discretized data

Abstract

We present a variant of the well sounded Expectation-Maximization Clustering algorithm that is constrained to generate partitions of the input space into high and low values. The motivation of splitting input variables into high and low values is to favour the semantic interpretation of the final clustering. The Expectation-Maximization binary Clustering is specially useful when a bimodal conditional distribution of the variables is expected or at least when a binary discretization of the input space is deemed meaningful. Furthermore, the algorithm deals with the reliability of the input data such that the larger their uncertainty the less their role in the final clustering. We show here its suitability for behavioural annotation of movement trajectories. However, it can be considered as a general purpose algorithm for the clustering or segmentation of multivariate data or temporal series.