Multi-view representation learning for data stream clustering

open access articleData stream clustering provides valuable insights into the evolving patterns of long sequences of continuously generated data objects. Most existing clustering methods focus on single-view data streams. In this paper, we propose a multi-view representation learning (MVRL) method for multi-view clustering of data streams. We first introduce an integrated representation learning model to learn a fused sparse affinity matrix across multiple views for spectral clustering. Motivated by the optimization procedure of the integrated representation learning model, we propose three consecutive stages: collaborative representation, the construction of individual global affinity matrices using a mapping function, and the calculation of a fused sparse affinity matrix using Euclidean projection. These stages allow the effective capture of the global and local structures of high-dimensional data objects. Moreover, each stage has a closed-form solution, which determines the upper bound of the computational cost and memory consumption. We then employ the construction residuals of the collaborative representation to adaptively update a dynamic set, which is used to preserve the representative data objects. The dynamic set efficiently transfers previously learned useful knowledge to the arriving data objects. Extensive experimental results on multi-view data stream datasets demonstrate the effectiveness of the proposed MVRL method

Unified Matrix Factorization with Dynamic Multi-view Clustering

Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item representation matrix. In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations. The above process, however, is time and computationally intensive, making it difficult to apply in real-time to e-commerce or Internet of Things environments with billions of users and trillions of items. To address this, we propose a unified matrix factorization method based on dynamic multi-view clustering (MFDMC) that employs an end-to-end training paradigm. Specifically, in each view, a user/item representation is regarded as a weighted projection of all clusters. The representation of each cluster is learnable, enabling the dynamic discarding of bad clusters. Furthermore, we employ multi-view clustering to represent multiple roles of users/items, effectively utilizing the representation space and improving the interpretability of the user/item representations for downstream tasks. Extensive experiments show that our proposed MFDMC achieves state-of-the-art performance on real-world recommendation datasets. Additionally, comprehensive visualization and ablation studies interpretably confirm that our method provides meaningful representations for downstream tasks of users/items

Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking

Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays