EPLS: A novel feature extraction method for migration data clustering

Nowadays human activity data such as migration data can be easily accumulated by personal devices thanks for GPS. Analysis on migration data is very useful for society decision. Migration data as non-line time series have the properties of higher noise and outliers. Traditional feature extraction methods cannot address this issue very well because of inherent characteristics. Aiming at this problem, a novel numerical feature extraction approach EPLS is proposed. It is an integration of the Ensemble Empirical Mode (EEMD), Principal Component Analysis (PCA) and Least Square (LS) method. The EPLS model includes (1) Mode Decomposition in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction is carried out for a more significant set of vectors; (3) Least Squares Projection in which all testing data are projected to the obtained vectors. Experimental results show that EPLS can overcome the higher noise and outliers based on migration data clustering. Meanwhile, EPLS feature extraction method can achieve high performance compared with several different clustering methods and distance measures.