Abstract: Heterogeneous information networks (HINs)—typed graphs with labeled nodes and edges—are popular in academia and industry. We discover the k most important meta paths in real time for friend search, product recommendation, anomaly detection, and…
Abstract: Matrix decomposition is essential for mining big data from modern applications. However, processing very large data on a single machine is inefficient or impossible. Big data are also distributed across multiple machines. Thus, heterogeneous…
Abstract: Several discretization algorithms have been developed, but efficient, accurate discretization remains a challenge. This paper proposes SPID5, a new discretization algorithm based on two supervised heuristics, one local and one global, that synergize. The…
Abstract: Temporal point process (TPP) modeling is expressive. TPP modeling rarely investigates event sequence clustering temporal patterns. Reinforcement learning assumes the observed sequences are generated by a mixture of latent policies to solve this problem….
Abstract: Data analysis involves community detection. Algorithms to solve this problem have been proposed for decades. Most community detection research ignores statistical significance. Although statistically significant communities have been mined, deriving an analytical solution of…
Abstract: Spatio-temporal data is increasingly available due to the rapid development of GPS, mobile devices, and remote sensing. Human mobility understanding, smart transportation, urban planning, public safety, health care, and environmental management depend on spatio-temporal…
Abstract: Adverse event detection is essential for identifying product defects, disasters, and major socio-political events. Adverse drug events cause many hospitalizations and deaths annually. Search query logs are a key detection channel because users start…
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….
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