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• An DBSCAN extension for online structural clustering is proposed. • Information granules are constructed to describe structures of shaped clusters. • Granular computing and rule-based modeling are applied for online clustering. • Good performances are achieve on accuracy and time in online structural clustering. In online structural clustering, general density-based clustering algorithms have problems of low scalability and high computation cost, especially in big data analysis, this paper proposed a DBSCAN extension algorithm with consideration of granule computing to handle these problems. This algorithm mainly makes use of advantages of DBSCAN and granular descriptors to realize effective and efficient structural online clustering. Frist, to extract structural clusters effectively, DBSCAN is considered as the basic clustering algorithm in this research. Second, since DBSCAN's results are not numerical for online testing, this paper proposes to apply granule computing (GrC) to construct information granules describing arbitrarily-shaped clusters from DBSCAN. Third, to realize an efficient online structural clustering, especially in big data analysis, a series of granular fuzzy models are built with consideration of structural information, then a rule-based model is formed for guiding online clustering of new testing data. Through the proposed method, the online clustering ability of DBSCAN is developed with reduced computation cost, meanwhile the structural clustering ability is also retained in online testing. Experiments on synthetic data, publicly available data and real-world data are discussed, online testing accuracy and computation time are evaluated to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A differential privacy-based nonnegative matrix factorization is proposed. • An objective perturbation is designed to limit the effect of noise. • Robustness of items with few ratings is improved by an imputation-based scheme. • The effectiveness of the proposed schemes on large datasets is theoretically analyzed. Nonnegative matrix factorization (NMF)-based models have been proven to be highly effective and scalable in addressing collaborative filtering (CF) problems in the recommender system (RS). Since RS requires tremendous user data to provide personalized information services, the issue of data privacy has gained prominence. Although the differential privacy (DP) technique has been widely applied to RS, the requirement of nonnegativity makes it difficult to successfully incorporate DP into NMF. In this paper, a differentially private NMF (DPNMF) method is proposed by perturbing the coefficients of the polynomial expression of the objective function, which achieves a good trade-off between privacy protection and recommendation quality. Moreover, to alleviate the influence of the noises added by DP on the items with sparse ratings, an imputation-based DPNMF (IDPNMF) method is proposed. Theoretic analyses and experimental results on several benchmark datasets show that the proposed schemes have good performance and can achieve better recommendation quality on large-scale datasets. Therefore, our schemes have high potential to implement privacy-preserving RS based on big data. [ABSTRACT FROM AUTHOR]

• Propose a feasible paradigm for using multidomain data in big data analysis. • Apply multigranularity analysis to small-scale low-dimensional data sets. • Transform prediction into a classification problem using deep forest computation. When addressing analysis and prediction problems in a specific domain based on big data processing, the following problems often arise: only relationships between features in the domain itself are considered, and existing methods are not effective for training models on small sample data sets. The traditional approach usually obtains the relationships between single-domain features. Analysis and forecasting in the problem domain alone can quickly achieve good accuracy, but due to the limitations of the analysis domain, it becomes increasingly difficult to further improve the prediction accuracy. This paper proposes a novel data analysis approach compatible with small sample sets called multidomain data depth analysis (MODE). In contrast to traditional approaches, MODE emphasizes multidomain data and considers the relationships among feature domains in the original data. The features in each domain are orthogonally extracted, and feature dimensions are expanded in accordance with the characteristics of small data sets. A better prediction model can be obtained by using the expanded and strengthened features. We apply this approach to real big data from the field of sociology to predict annual income based on census data in experiments. The experimental results show that MODE offers a better prediction effect based on small multidomain samples. [ABSTRACT FROM AUTHOR]