Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorized as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns (EPs) are interesting in a data stream context as easy, fast, and reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory, and continuous model updates. In this article, an approach for the extraction of EPs in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential.

中文翻译：

---

FEPDS：数据流中模糊新兴模式提取的提案


如今，大多数数据是由连续产生数据的设备生成的。这些类型的数据可以归类为数据流，可以从中提取有价值的见解。特别是，新兴模式 (EP) 提取的见解在数据流环境中非常有趣，因为可以做出简单、快速且可靠的决策。然而，由于必要的响应时间、内存和持续的模型更新，它们的提取是一个挑战。在本文中，提出了一种在数据流中提取 EP 的方法。它按照自适应方法通过批量处理实例。学习算法是一种进化模糊系统，其中利用先前的知识来适应概念漂移。为了证明该方法在对抗概念漂移方面的适用性和所提取知识的质量，已经进行了广泛的实验研究。最后，该提案应用于一个与根据票价金额持续确定纽约市出租车客户资料相关的案例研究，以显示其潜力。