Unsupervised learning is one of the fundamental machine learning methods. Clustering is a vital unsupervised learning task and can significantly contribute to the detection of hidden structures in unknown datasets. Clusterability is an important concept due to the fact that it can theoretically portray the extent to which a clustering algorithm can recover a benchmark clustering, with the absence of excessive experimental validations. Moreover, conventional batch-mode-clustering-oriented clusterability analysis should be extended to the incremental setting when the clustering algorithm is required to handle stream data. However, such clusterability analysis is facing two barriers. First, the incremental clustering algorithm proceeds in a step-wise manner and can merely access the newly arrived data of the current step. This extremely fragmentary view of the entire input data stream inevitably results in a biased perception of the underlying benchmark clustering. Second, incremental clustering is conventionally applied to real-time or massive-data scenarios. Such application scenarios typically require the computational power of mainstream SIMD (Single Instruction Multiple Data) hardware accelerators. However, strong data dependency inherently exists between two successive steps of an incremental clustering algorithm, which dramatically impairs data parallelism. In view of these constraints, we propose our roadmap to theoretically analyze and ensure the clusterability under an incremental setting in terms of a general clusterability metric: niceness (higher intra-cluster similarity than inter-cluster similarity). In our work, a nice-k clustering (a clustering that has k clusters and satisfies the niceness metric) is supposed to exist in the input data stream. Meanwhile, the input data stream is supposed to be divided into a series of micro-clusters, and the micro-clusters are incrementally clustered into clusters. In addition, we rely on an assumption (homogeneity assumption) that every micro-cluster merely contains homogenous data. First, we point out that a vital reason for the induction of heterogeneous clusters is the lack of representative micro-clusters. We propose Theorem 1 to iteratively identify a set of 2[Formula: see text] representative micro-clusters that can cover all k benchmark clusters. Therefore, we can trade the number of clusters for homogeneity and thus assure clusterability. Second, we demonstrate that evolution in the granularity of a micro-cluster can prompt SIMD-parallelism more than in the granularity of a single data point. Consequently, the clusterability-assured method of Theorem 1 is furthermore parallel-friendly. In all, we depict a roadmap to assure clusterability under both incremental and SIMD-friendly constraints.

Abstract Anomaly event detection is vital in surveillance video analysis. However, how to learn the discriminative motion in the crowd scene is still not tackled. Here, a deep social force network by exploiting both social force extracting and deep motion coding is proposed. Given a grid of particles with velocity provided by the optical flow, the interaction force in the crowd scene is investigated and a social force module is embedded in a deep network. A deep motion convolution was further designed with a 3D (DMC‐3D) module. The DMC‐3D not only eliminates the noise motion in the crowd scene with a spatial encoder–decoder but also learns the 3D feature with a spatio‐temporal encoder. The deep social force coding is modelled with multiple features, in which each feature can describe specific anomaly motion. The experiments on UCF‐Crime and ShanghaiTech datasets demonstrate that our method can predict the temporal localization of anomaly events and outperform the state‐of‐the‐art methods.

As a classical clustering model, Gaussian Mixture Model (GMM) can be the footstone of dominant machine learning methods like transfer learning. Evolving GMM is an approximation to the classical GMM under time-critical or memory-critical application scenarios. Such applications often have constraints on time-to-answer or high data volume, and raise high computation demand. A prominent approach to address the demand is GPGPU-powered computing. However, the existing evolving GMM algorithms are confronted with a dilemma between clustering accuracy and parallelism. Point-wise algorithms achieve high accuracy but exhibit limited parallelism due to point-evolutionary pattern. Block-wise algorithms tend to exhibit higher parallelism. Whereas, it is challenging to achieve high accuracy under a block-evolutionary pattern due to the fact that it is difficult to track evolving process of the mixture model in fine granularity. Consequently, the existing block-wise algorithm suffers from significant accuracy degradation, compared to its batch-mode counterpart: the standard EM algorithm. To cope with this dilemma, we focus on the accuracy issue and develop an improved block-evolutionary GMM algorithm for GPGPU-powered computing systems. Our algorithm leverages evolving history of the model to estimate the latest model order in each incremental clustering step. With this model order as a constraint, we can perform similarity test in an elastic manner. Finally, we analyze the evolving history of both mixture components and the data points, and propose our method to merge similar components. Experiments on real images show that our algorithm significantly improves accuracy of the original general purpose bock-wise algorithm. The accuracy of our algorithm is at least comparable to that of the standard EM algorithm and even outperforms the latter under certain scenarios.

Model transformations are touted to play a key role in model-driven software development. The mapping relations between different models are the foundation and basis for the transformation. A classification for different level mappings was proposed and defined formally by abstractly analyzing the characteristics of syntax and semantic features of modeling languages. On this basis, a further study about the simulation-based mapping approach was conducted to explore the definition process for mapping relations and the cardinal principles should be followed. The UML-based class model used as a source and the C programming language used as the target are shown in the case study to help interpreting the ideas. It may not only be a theoretical guidance for model transformation, but also can be a measurement for validating the mapping rules between models at different abstract levels.

The transformation from platform independent models to platform specific models is a key technology in OMG's MDA. The mapping relations between different models are the foundation and basis for the transformation. By abstractly analyzing the characteristic of syntax and semantics of modeling languages, a semantic-features-calculation based model mapping approach for the development of Web information systems was proposed. To using this approach, abstract target semantic model should be constructed firstly. Then, based on the idea of elements in source semantic domain being reconstructed in the target semantic domain, mapping relations from source model to target model are made via abstract target semantic model. This approach may not only to be a theoretical guidance for model transformation, but also can be a measurement for validating the mapping rules between models at different abstract levels. JavaServer Faces assisted with Enterprise JavaBeans was used as a target platform to help interpreting the process of using this approach.