L
Lian Duan
Researcher at Hofstra University
Publications - 28
Citations - 2106
Lian Duan is an academic researcher from Hofstra University. The author has contributed to research in topics: Local outlier factor & Cluster analysis. The author has an hindex of 17, co-authored 28 publications receiving 1801 citations. Previous affiliations of Lian Duan include Chinese Academy of Sciences & New Jersey Institute of Technology.
Papers
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Big data for cyber physical systems in industry 4.0: a survey
TL;DR: This survey conducts this survey to bring more attention to this critical intersection between cyber physical systems and big data and highlight the future research direction to achieve the fully autonomy in Industry 4.0.
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Healthcare information systems: data mining methods in the creation of a clinical recommender system
Lian Duan,W.N. Street,Eric Xu +2 more
TL;DR: The proposed system uses correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans, and utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items.
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Business Intelligence for Enterprise Systems: A Survey
Lian Duan,Li Da Xu +1 more
TL;DR: This paper points out the challenges and opportunities to smoothly connect industrial informatics to enterprise systems for BI research and plays a very important role to bridge the connection between enterprise systems andindustrial informatics.
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A local-density based spatial clustering algorithm with noise
TL;DR: A new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed, which takes the advantage of the LOF to detect the noises comparing with other density-based clustering algorithms.
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Cluster-based outlier detection
TL;DR: A new definition for outliers is presented: cluster-based outlier, which is meaningful and provides importance to the local data behavior, and how to detect outliers by the clustering algorithm LDBSCAN which is capable of finding clusters and assigning LOF.