Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things (IoT) are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized and thus help manufacturing organizations to finish another round of upgrading. In this paper, we propose two new algorithms with respect to big data analysis, namely UFC$_{gen}$ and UFC$_{fast}$. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFC$_{fast}$ algorithm outperforms the level-wise-based UFC$_{gen}$ algorithm in terms of both execution time and memory consumption.

本文研究采用大数据技术设计新的UFC$_{gen}$和UFC$_{fast}$算法，用于收集三种类型的模式，以帮助人们确定不同产品组合的市场位置，并在真实和模拟数据集上比较这两种算法，结果表明基于列表的UFC$_{fast}$算法在执行时间和内存消耗方面优于基于层次的UFC$_{gen}$算法。

智能系统：联合功用和频率进行模式分类