In recent years, the coastal ports of the Yangtze River Delta have rapidly developed with the progress of science and technology, which has caused some problems on account of the rapid development of ports. On the one hand, there is fierce competition within the same port group; on the other hand, many ports waste resources. This study selected the three-stage data envelopment analysis (DEA) and Malmquist index models to calculate and analyze the efficiency value of the coastal port group in the Yangtze River Delta; the study was conducted to make a reference for the formulation of the optimization strategy from the perspectives of static and dynamic efficiency. The results show that from the perspective of static efficiency, the comprehensive efficiency of the Yangtze River Delta coastal port cluster is at the upper-middle level. However, it has not yet reached the frontier surface, and the low scale efficiency is why the port group has not been called the frontier surface. From the perspective of dynamic efficiency, the total factor productivity of the Yangtze River Delta port group has increased by 3.6% in the past five years. Technological progress and comprehensive technical efficiency have improved. The optimization strategy was formulated according to the problems faced by the Yangtze River Delta port group and the reasons for not reaching the frontier.

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Unsupervised shapelets (u-shapelets) are time series subsequences that can best separates between time series coming from different clusters of data set without label. Because of the high computational cost, the u-shapelets are prohibited for many large dataset. Nevertheless, almost all of the current methods try to improving the u-shapelets based clustering method through reducing the computation time of u-shapelets candidate set. In this paper, we proposed a novel method improving efficiency of u-shapelets in terms of improving the u-shapelets quality. There are three contributions in our work: firstly, we show that by using internal evaluation measure instead gap score can improve quality of u-shapelets. Secondly, a novel method was proposed that applying diversified top-k query technology to filter similar u-shapelets, especially selecting the k most representative u-shapelets on the entirely shapelets candidates. Lastly, extensive experimental results show that combining internal evaluation measure and diversified top-k u-shapelets technology, our proposed method outperforms not only u-shapelet based methods, but also typical time series clustering approaches.