• Publications
  • Influence
Secure kNN computation on encrypted databases
TLDR
A new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product and is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Expand
Maintenance of discovered association rules in large databases: an incremental updating technique
TLDR
An incremental updating technique is developed for maintenance of the association rules discovered by database mining when new transaction data are added to a transaction database. Expand
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
TLDR
A connectivity-based outlier factor (COF) scheme is introduced that improves the effectiveness of an existing local outlier factors (LOF) scheme when a pattern itself has similar neighbourhood density as an outlier. Expand
A fast distributed algorithm for mining association rules
TLDR
An interesting distributed association rule mining algorithm, FDM (fast distributed mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules is proposed. Expand
A General Incremental Technique for Maintaining Discovered Association Rules
TLDR
The study shows that the new incremental algorithm is signijcantly faster than the traditional approach of mining the whole updated database, and compared with the best algorithms for mining association rules studied so far. Expand
Mining, indexing, and querying historical spatiotemporal data
TLDR
This work defines the spatiotemporal periodic pattern mining problem and proposes an effective and fast mining algorithm for retrieving maximal periodic patterns, and devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotsemporal queries. Expand
Mining uncertain data with probabilistic guarantees
TLDR
This paper proposes two effcient algorithms, which discover frequent patterns in bottom-up and top-down manners and explains how to use these patterns to generate association rules. Expand
HARP: a practical projected clustering algorithm
TLDR
A new algorithm is proposed that exploits the clustering status to adjust the internal thresholds dynamically without the assistance of user parameters and has excellent accuracy and usability. Expand
Mining frequent spatio-temporal sequential patterns
TLDR
This paper proposes algorithms to find patterns by employing a newly proposed substring tree structure and improving a priori technique, and defines pattern elements as spatial regions around frequent line segments. Expand
Non-homogeneous generalization in privacy preserving data publishing
TLDR
This work provides a methodology for verifying whether a non-homogeneous generalization violates k-anonymity, and proposes a randomization method that prevents this type of attack and shows that k-Anonymity is not compromised by it. Expand
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