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Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.

Predicting treatment medicines is a key aspect of many intelligent healthcare systems. It's a very challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically include laboratory results, treatment medicines, disease conditions, and demographic details collected from disparate sources; (2) complex correlations among medical sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal diversity of these correlations, which is highly affected by changing disease progression. We proposes a dual adaptive sequential network, entitled DASNet, to dynamically predict treatment medicines for patients. Specifically, DASNet comprises the following three components. Decomposed Adaptive Long Short-Term Memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an Attentive Meta learning Network (AT-MetaNet), which produces location- and context-specific dynamic weight parameters for DA-LSTM, thus enabling it to model the time-varying multi-level correlations. Finally, we employ an ATtentive Fusion Network (AT-FuNet) to retrieve historical information and collectively fuse heterogeneous data representation embeddings to predict treatment medicines. The results of extensive experiments on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights.

The authors investigate how peer influence affects customers' product adoption behaviors in emerging video game platforms. Understanding peer influence is critical to motivating users' willingness to purchase and improving game publishers' marketing performance. While similarities between socially linked users can be viewed as a consequence of social influence, homophily may also contribute to such phenomenon, causing identification difficulties in observational studies. Using data from the world's largest digital distribution platform for video games, the authors leverage state-of-the-art recommender system algorithms and propose an innovative framework to identify social influence in the adoption of video games when a confounding homophily effect is present. The results show that peer influence has a positive impact on platform users' adoption behaviors (i.e., a user tends to adopt a video game that has been purchased by his peers). This study also finds that peer influence would have been overestimated if homophily was not properly controlled.

The understanding of people’s inter-regional mobility behaviors, such as predicting the next activity region (AR) or uncovering the intentions for regional mobility, is of great value to public administration or business interests. While there are numerous studies on human mobility, these studies are mainly from a statistical view or study movement behaviors within a region. The work on individual-level inter-regional mobility behavior is limited. To this end, in this article, we propose a dynamic region-relation-aware graph neural network (DRRGNN) for exploring individual mobility behaviors over ARs. Specifically, we aim at developing models that can answer three questions: (1) Which regions are the ARs? (2) Which region will be the next AR, and (3) Why do people make this regional mobility? To achieve these tasks, we first propose a method to find out people’s ARs. Then, the designed model integrates a dynamic graph convolution network (DGCN) and a recurrent neural network (RNN) to depict the evolution of relations between ARs and mine the regional mobility patterns. In the learning process, the model further considers peoples’ profiles and visited point-of-interest (POIs). Finally, extensive experiments on two real-world datasets show that the proposed model can significantly improve accuracy for both the next AR prediction and mobility intention prediction.