With the widespread use of GPS devices and the advancement of location-based services, a vast amount of trajectory data has been collected and mined for various applications. Trajectory clustering, which categorizes trajectories into distinct groups, is the fundamental functionality of trajectory data mining. The challenge is how to cluster on a mass of trajectory data efficiently and universally with satisfying results. The raw trajectory clustering algorithms are universal, but trapped in the dilemma between efficiency and desirable results. Other approaches, such as density-based, road network-based, and deep learning-based algorithms, encounter issues like high time complexity, loss of trajectory integrity, reliance on road networks, and data quality during training. To tackle these challenges, we first propose the efficient KMCT (k-Means Clustering of Trajectories) algorithm based on a semantic interpolation transformation to cluster raw trajectories and achieve satisfying results. Additionally, we introduce the DA-KMCT (Density Accelerated k-Means Clustering of Trajectories) algorithm to further boost the clustering process based on trajectory densities and an optimized centroid selecting strategy. Moreover, we present a novel clustering evaluation method called IOD, which efficiently estimates clustering results on large-scale datasets with linear time complexity. Experimental results on real-world datasets demonstrate that KMCT and DA-KMCT outperform five related methods in terms of clustering quality and time efficiency, and the proposed IOD evaluation shows a strong correlation with the Silhouette Coefficient, offering a reliable and efficient alternative for evaluating clustering results.

Time series classification assigns predefined labels or classes to sequences of data points ordered chronologically, which is a fundamental task for time series analysis. Existing time series classification methods mainly focus on a specific type of time series (i.e., univariate time series or multivariate time series), while failing to support both of them efficiently and effectively. In addition, most of existing multivariate time series classification methods model all variables collectively, resulting in protracted computational times and suboptimal accuracy. In this paper, we introduce TSec, an innovative time series classification framework that exhibits high training efficiency and classification accuracy for both univariate time series and multivariate time series. During online classification, TSec first involves sequence segmentation and de-duplication, and then employs pre-trained models to perform classifications. To opti-mize the classification performance, TSec (i) utilizes correlation analysis to reveal closely interconnected groups of variables within multivariate time series data; (ii) incorporates time series alignment and different sliding windows to generate potential shapelets; (iii) applies PAA and SAX techniques to eliminate duplicates, thereby enhancing the quality of shapelets; (iv) adopts Bi-GRU and GCN-GRU models to effectively capture the characteristics of the two types of time series. Extensive experiments on 112 public univariate time series datasets and 26 public multivariate time series datasets show that TSec can achieve both high efficiency and accuracy compared with the state-of-the-art 19 toolkits.

Abstract As a fundamental part of indoor scene understanding, the research of indoor room layout estimation has attracted much attention recently. The task is to predict the structure of a room from a single image. In this paper, we illustrate that this task can be well solved even without sophisticated post-processing program, by adopting Feature Pyramid Networks (FPN) to solve this problem with adaptive changes. The proposed model employs two strategies to deliver quality output. First, it can predicts the coarse positions of key points correctly by preserving the order of these key points in the data augmentation stage. Then the coordinate of each corner point is refined by moving each corner point to its nearest image boundary as output. Our method has demonstrated great performance on the benchmark LSUN dataset on both processing efficiency and accuracy. Compared with the state-of-the-art end-to-end method, our method is two times faster at processing speed (32 ms) than its speed (86 ms), with 0.71% lower key point error and 0.2% higher pixel error respectively. Besides, the advanced two-step method is only 0.02% better than our result on key point error. Both the high efficiency and accuracy make our method a good choice for some real-time room layout estimation tasks.

There is an emerging trend of integrating machine learning (ML) techniques into database systems (DB). Considering that almost all the ML toolkits assume that the input of ML algorithms is a single table even though many real-world datasets are stored as multiple tables due to normalization in DB. Thus, data scientists have to perform joins before learning a ML model. This strategy is called learning after joins, which incurs redundancy avoided by normalization. In the area of ML, the Support Vector Machine (SVM) is one of the most standard classification tools. In this paper, we focus on the factorized SVM with gaussian kernels over normalized data. We present factorized learning approaches for two main SVM optimization methods, i.e., Gradient Descent (GD) and Sequential Minimal Optimization (SMO), by factorizing gaussian kernel function computation. Furthermore, we transform the normalized data into matrices, and boost the efficiency of SVM learning via linear algebra operations. Extensive experiments with nine real normalized data sets demonstrate the efficiency and scalability of our proposed approaches.