Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part 1

Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. More specifically, pattern-driven mining, analytics, and prediction have received a lot of attention in the last two decades since information discovered in data can be used to support decision and strategy making. The results can also be utilized in decision support or information management systems (IMS). Different types of patterns and knowledge can be mined (extracted) from various applications and domains. Many previous studies focused on designing and implementing new methodologies to handle different constraints and requirements. This special issue focuses on discovering the knowledge, rules, and information for decision support and management information systems. Innovative methodologies, principles, methods, techniques, framework, theory, and applications are thus considered to deal with the challenges for decision support and management information systems. In this special issue there were 47 submissions. For Part 1, we are publishing eight articles, with more planned for a future issue. All accepted manuscripts have made a significant scientific contribution and presented a rigorous evaluation of the Information Systems outcomes in real-world practices and analysis. The summary of the accepted papers is stated below. InDSSAE: Deep stacked sparse autoencoder analytical model for COVID-19 diagnosis by fractional Fourier entropy [1], the authors proposed a novel artificial intelligence model to diagnose COVID19 based on chest CT images. First, the two-dimensional fractional Fourier entropy was presented to extract features. A custom deep-stacked sparse autoencoder (DSSAE) model was then created to serve as the classifier. Improved multiple-way data augmentation was proposed to resist overfitting. Results showed that the designed DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem. In addition, the designed model outperforms 10 state-ofthe-art approaches. In TRG-DAtt: The target relational graph and double attention network based sentiment analysis and prediction for supporting decision making [2], the authors designed a TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information for supporting decision making. A dependency tree-based TRG is firstly introduced to independently and fully mine the semantic relationships. A dependency graph attention network (DGAT) is then designed, as well as the interactive attention network (IAT) to form the DAtt, and obtained the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information, and the target emotional enhancement features are obtained by the DGAT as an input to the IAT.