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Customer clustering, the division of customers into different groups, is a classical problem. It is especially important in banking as it serves multiple purposes in marketing, risk management, etc. Therefore, it has attracted the use of many modern machine learning models and techniques. But currently, most of them are only making use of "static" customer information. This paper proposes a new approach for customer clustering in banking based on the customers' balance history. Basic Dynamic Time Warping (DTW) distance and Soft-DTW (SDTW) distance, an advanced form, are used to measure the difference between customers. To which, the two most popular strategies in time series clustering strategies, partitional and hierarchical, are applied which. In additional, some statistical features are given to prove the effectiveness of the proposed method.

Gas sensors are instrumental in the control and monitoring of air pollution. A facile fabrication method for low-cost gas sensors with high sensitivity and a fast response time is crucial in practical applications. Here, reduced graphene oxide (rGO)–CuO nanocomposites were synthesized for gas-sensing applications using a facile hydrothermal method. The crystal structure, surface morphology, and electrical properties of the nanocomposites were inferred from X-ray powder diffraction patterns, scanning electron microscopic images, and current–voltage (I–V) measurements, respectively. The results confirmed a high-quality rGO–CuO material with a spherical flower-like morphology. The CuO material showed a single-phase monoclinic crystal structure with an average crystal size of ~21 nm. Within the composite, high-quality rGO was incorporated into the porous spherical flower-like CuO material. In gas-sensing measurements, the rGO–CuO nanocomposite detected NO2 gas at low concentrations (1–5 ppm) with reasonably high response values and a fast response time (<1 min). An rGO–CuO nanocomposite-based sensor was fabricated, showing good repeatability for practical applications.

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