Learning Representations For Incomplete Time Series Clustering

Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missing values. Traditional strategy (imputing first and then clustering) does not optimize the imputation and clustering process as a whole, which not only makes performance dependent on the combination of imputation and clustering methods but also fails to achieve satisfactory results. How to best improve the clustering performance on incomplete time series remains a challenge. This paper proposes a novel unsupervised temporal representation learning model, named Clustering Representation Learning on Incomplete time-series data (CRLI). CRLI jointly optimizes the imputation and clustering process to impute more discriminative values for clustering and make the learned representations possessed good clustering property. Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating training manner. An experiment conducted on eight real-world incomplete time-series datasets shows that CRLI outperforms existing methods. We demonstrate the effectiveness of the learned representations and the convergence of the model through visualization analysis. Moreover, we reveal that the joint training strategy can impute values close to the true ones in those important sub-sequences, and impute more discriminative values in those less important sub-sequences at the same time, making the imputed sequence cluster-friendly.