Symbolic representation and clustering of bio-medical time-series data using non-parametric segmentation and cluster ensemble

Representation of time series data is a fundamental problem that impacts data analysis in many problems of medicine and life sciences. Due to the inherent high dimensionality of time-series data, dimensionality reduction constitutes one of the important requirements for a successful representation. Piecewise aggregate approximation (PAA) of time series is a widely used approach in this context. In PAA, the time-series is represented as a set of points where each point is defined as the average of the original data points that it represents. Though highly promising, PAA requires estimating the number of segments required for the representation a priori. In this paper we propose a data driven method for optimally and non-parametrically segmenting a timeseries. Instead of depending on a single estimation of the segments, we employ multiple methods and combine the results using an ensemble approach. This allows the proposed approach to be robust for different types of data. With difficult biomedical time series data sets, experimental results show that our method estimated the optimal segments correctly and achieved the highest accuracy in a number of unsupervised time-series classification tasks.