Related papers: Sequen-C: A Multilevel Overview of Temporal Event …

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale,…

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics…

The rapid growth and availability of event sequence data across domains requires effective analysis and exploration methods to facilitate decision-making. Visual analytics combines computational techniques with interactive visualizations,…

Real-world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed…

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific…

Comparative analysis of event sequence data is essential in many application domains, such as website design and medical care. However, analysts often face two challenges: they may not always know which sets of event sequences in the data…

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy…

Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be…

With modern IVIS becoming more capable and complex than ever, their evaluation becomes increasingly difficult. The analysis of large amounts of user behavior data can help to cope with this complexity and can support UX experts in designing…

Parallel event sequences, such as those collected in program execution traces and automated manufacturing pipelines, are typically visualized as interactive parallel timelines. As the dataset size grows, these charts frequently experience…

Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present…

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision…

Multimedia data is highly expressive and has traditionally been very difficult for a machine to interpret. Middleware systems such as complex event processing (CEP) mine patterns from data streams and send notifications to users in a timely…

Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC),…

Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and so forth. This observation is equally valid for various…

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised…

Multiview clustering (MVC) segregates data samples into meaningful clusters by synthesizing information across multiple views. Moreover, deep learning-based methods have demonstrated their strong feature learning capabilities in MVC…

Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism…

Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision. Due to the spatio-temporal asynchronism,…

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on the predefined clustering criterion, but devising such a…