A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

Mining Clinical Process from Hospital Information System: A Granular Computing Approach

Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.

Transformations and information granularity of knowledge structures in set-based granular computing

Granular computing is a relatively new platform for constructing, describing and processing information or knowledge. For crisp information granulation, the universe is decomposed into granules by binary relations on the universe, say, preorder, tolerance and equivalence relations. A knowledge structure is composed of all information granules induced by a relation that corresponds to the granulation. This paper establishes a novel theoretical framework for the measurement of information granularity of knowledge structures. First, two new relations between knowledge structures are introduced through the use of their respective Boolean relation matrices, where the granular equality relation is defined based on an orthogonal transformation with the transformation matrix being a permutation matrix, and the granularly finer relation is presented by combining the classical finer relation and the orthogonal transformation. Then, it is demonstrated that the simplified knowledge structure base with the granularly finer relation is a partially ordered set, which can be represented by a Hasse diagram. Subsequently, an axiomatic definition of information granularity is proposed to satisfy the constraints regarding these two relations. Moreover, a general form of the information granularity is given, and some existing measures are proved to be its special cases. Finally, as an application of the proposed measure, the attribute significance measure is developed based on the information granularity.

Hierarchies and Uncertainty Measures on Pythagorean Fuzzy Approximation Spaces

In Granular Computing, the hierarchies and uncertainty measures are two important concepts to investigate the granular structures and uncertainty of approximation spaces. In this paper, hierarchies and uncertainty measures on pythagorean fuzzy approximation spaces will be researched. Firstly, the introduction and operations of pythagorean fuzzy granular structures are given, and three hierarchies and a lattice structure on pythagorean fuzzy approximation spaces are examined. The hierarchies are characterized by three order relations, the first order relation is defined on the inclusion relation of pythagorean fuzzy information granules, the second one is defined on the cardinality of pythagorean fuzzy information granules, and the third one is defined on the sum of the cardinality of pythagorean fuzzy information granules. The lattice structure is constructed on the first order relation on pythagorean fuzzy approximation spaces. Fuzzy information granularity and fuzzy information entropy are extended to describe the uncertainty of pythagorean fuzzy granular structures, and the relationship between the uncertainty measures and hierarchies are discussed. The examples show that hierarchies are effective to analyze the relationships among all granular structures on pythagorean fuzzy approximation spaces.