Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

中文翻译：

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用于用户行为分类和预测的神经模糊方法

由于近十年来社交媒体使用量的快速增长，大数据和云计算技术作为新趋势出现在了现场。大数据代表了大量的复杂数据，这些数据显示了有关世界各地发生的行为，活动和事件的更多详细信息。因此，大数据分析需要在减少的响应时间内访问各种类型的资源，以进行准确而稳定的业务试验，从而可以帮助组织实时做出明智的决策。这些发展刺激了研究，发明和商业营销的革命性转变。用于分类和预测的用户行为分析是数据科学中最热门的主题之一。执行此类型的分析有多种目的，例如找到用户对某种产品（用于营销，电子商务等）或对某个事件（选举，锦标赛等）的兴趣，并根据他们在Internet上的特征观察可疑活动（安全性和隐私权）。本文提出了一种用于用户行为分类和预测的神经模糊方法。目标是一个数据集，该数据集由用户的时间日志组成，其中包含三种类型的信息，即本地计算机日志，网络日志和Web使用日志。为了补充分析，还利用了每个用户的360度反馈。已实施各种规则来解决公司确定用户准确行为的政策，这可能有助于管理决策。为了预测，基于由基于模糊规则的系统（FRBS）生成的示例集和用户的360度反馈来训练高斯径向基函数神经网络（GRBF-NN）。获得了结果并将其与文献中的其他最新方案进行了比较，发现该方案在分类和预测准确性方面都很有前途。