Java Projects

Abstract: Many multi-view clustering algorithms have been proposed as real-world applications use multi-view data. Existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of hidden…

Abstract: Multiset membership lookup takes an item e as input and outputs a binary answer whether e is in S and, if so, the subset ID. Multiset membership lookup, more advanced than canonical membership lookup,…

Abstract: This paper discusses semi-supervised learning instance and feature co-selection. Co-selection is harder when data contain labeled and unlabeled examples from the same population. sCOs, a unified framework, efficiently integrates labeled and unlabeled parts into…

Abstract: Most advanced scientific, business, and social media applications use online analytics to efficiently execute large numbers of Aggregate Continuous Queries (ACQs). ACQs aggregate streaming data and periodically calculate max or average over a window…

Abstract: Many trajectories have user ID, location ID, time-stamp, and activity type (like “topic” in text mining) due to the widespread use of positioning devices. Existing works use latent activity types from topic models or…

Abstract: Pattern mining (PM) finds user-interested data patterns. Most studies have examined one pattern, like frequent or high-utility. Single-objective PM methods struggle to meet the growing needs of businesses in various industries. This paper proposes…

Abstract: Heterogeneous information networks (HINs) model objects of different types and their relationships. HIN objects usually have more attributes to enrich their data. AHINs are attributed HINs. We cluster objects in an AHIN based on…

Abstract: HIN embedding learns low-dimensional representations of nodes while preserving HIN structures and semantics. Most methods consider heterogeneous relations and perform well, but they use one model for all relations, which limits HIN embedding. We…

Abstract: GPS and wireless technology generate massive trajectory data. LBSN activity trajectory adds user semantic activities like visiting work/home/entertainment places to traditional trajectory data. Comparing activity trajectories in time, location, and semantics measures their similarity….

Abstract: Representation learning has helped machine learning models excel in many tasks, including machine translation, face recognition, and recommendation. Most representation learning methods require many consistent and noise-free labels. In real life, labels are limited…

Abstract: Density-based clustering methods identify intrinsic clusters by classifying dataset regions as high- or low-density based on neighborhood information. They automatically count clusters of any shape and are popular and effective. Datasets from different applications…

Abstract: Heterogeneous information networks (HIN) can model complex real-world data because their nodes and edges have different semantic types. Thus, HIN embedding, which learns node representations in a low-dimensional space to preserve HIN structural and…