论文标题
智能系统：图案分类的联合实用程序和频率
Smart System: Joint Utility and Frequency for Pattern Classification
论文作者
论文摘要
如今，用于行业4.0和物联网（IoT）的智能系统的环境正在经历快速的工业升级。开发了设计制造，事件检测和分类等大数据技术，以帮助制造组织实现智能系统。通过应用数据分析，可以最大化富数据的潜在值，从而帮助制造组织完成另一轮升级。在本文中，我们针对大数据分析提出了两种新算法，即UFC $ _ {gen} $和UFC $ _ {fast} $。两种算法都旨在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。我们比较了实际和合成的各种数据集上的这些算法。实验结果表明，这两种算法都可以通过基于用户指定的实用程序和频率阈值来利用所有候选模式的三种不同类型的有趣模式来成功实现模式分类。此外，就执行时间和内存消耗而言，基于列表的UFC $ _ {fast} $算法优于基于级别的UFC $ _ {gen} $算法。
Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things (IoT) are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized and thus help manufacturing organizations to finish another round of upgrading. In this paper, we propose two new algorithms with respect to big data analysis, namely UFC$_{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.
