Abstract: Text data analysis requires text clustering, which the text mining community has extensively studied. Most text clustering algorithms use the bag-of-words model, which ignores text structural and sequence information and is high-dimensional and sparse….
Abstract: Data mining and machine learning use matrix factorization (MF), an unsupervised data representation technique. Different application scenarios can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for…
Abstract: This paper examines a continuous query processing trajectory similarity search variant. For online detour detection, we compare the reference route Tr to the current partial route at each timestamp for a moving object from…
Abstract: Over the past two decades, recommender systems have used context awareness to offer consumers both top-rated and context-appropriate products. Context-aware service recommendation (CASR) systems use invocation time, location, social profiles, connectivity, and other context…
Abstract: Multimedia, machine learning, and data mining communities are focusing on multi-view clustering. Multi-view subspace clustering (MVSC) is a popular multi-view clustering algorithm because it can reveal the intrinsic low-dimensional clustering structure hidden across views….
Abstract: Data uncertainty is a major issue in database systems due to application inaccuracies. Probabilistic databases can store uncertain data and provide confident answers through querying. When systems propagate uncertainty, query or mining results may…
Abstract: KNN is a popular data mining algorithm. It has been successfully used for data analysis in many computer science research areas. This paper shows that KNN classification, despite its success, faces many challenges, including…
Abstract: A two-stage learning scheme must capture consensus cluster structures for graph-based multi-view clustering. Learn similarity graph matrices of multiple views, then combine them into a superior graph matrix. Most current methods learn pairwise similarities…
Abstract: Many reviews-rating integration studies have improved recommendation performance. These works still have flaws: (1) Dynamically integrating review and interaction data features is often overlooked, but treating them equally may lead to a misunderstanding of…
Abstract: User identification across multiple datasets has many applications, and recent research has increased on this topic. Most existing works identify users with a single input data type, such as (I) identifying a user across…
Abstract: Data matter. How can we quantitatively and objectively evaluate data? Pricing data, or information goods, has been studied and practiced in many disciplines, including economics, marketing, electronic commerce, data management, data mining, and machine…
Abstract: Finding the densest lasting subgraphs in large dynamic graphs, which takes into account subgraph pattern duration, is an important but understudied problem. Expectation-Maximization with Utility functions (EMU) is a stochastic framework that extends the…
WhatsApp us