The Experts below are selected from a list of 2649 Experts worldwide ranked by ideXlab platform

Wang Guoyin - One of the best experts on this subject based on the ideXlab platform.

  • Spatio-temporal Association Rule Mining Algorithm and its Application in Intelligent Transportation System
    Computer Science, 2011
    Co-Authors: Wang Guoyin
    Abstract:

    Taking into account the spatial and temporal constraints simultaneously can filter irrelevant data early and improve the efficiency of discovering spatio-temporal association rule.Based on the idea,Spatio-Temporal Apriori(STApriori) algorithm was proposed.It analyzes the time validity and spatial relativity at the same time during the gene-ration of frequency item sets.It classifies the time duration of spatio-temporal data and considers the spatial relationship firstly and generates the Transaction Table,then performs join operation on spatial-related item sets.Experiments illuminate that the algorithm is well performed.The algorithm is applied in intelligent transportation system to analyze the trend of traffic congestion by identifying spatio-temporal association between road sections.

Xiaoyang Wen - One of the best experts on this subject based on the ideXlab platform.

  • A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images
    International Journal of Applied Earth Observation and Geoinformation, 2015
    Co-Authors: Cunjin Xue, Wanjiao Song, Lijuan Qin, Qing Dong, Xiaoyang Wen
    Abstract:

    Abstract A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining Transaction Table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information Table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a “ Dimension–Attributes ” visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.

Hung-yu Kao - One of the best experts on this subject based on the ideXlab platform.

  • GrC - Hiding Sensitive Association Rules on Stars
    2010 IEEE International Conference on Granular Computing, 2010
    Co-Authors: Shyue-liang Wang, Tzung-pei Hong, Yu-chuan Tsai, Hung-yu Kao
    Abstract:

    Current technology for association rules hiding mostly applies to data stored in a single Transaction Table. This work presents a novel algorithm for hiding sensitive association rules in data warehouses. A data warehouse is typically made up of multiple dimension Tables and a fact Table as in a star schema. Based on the strategies of reducing the confidence of sensitive association rule and without constructing the whole joined Table, the proposed algorithm can effectively hide multi-relational association rules. Examples and analyses are given to demonstrate the efficacy of the approach.

  • ISDA - Multi-Table association rules hiding
    2010 10th International Conference on Intelligent Systems Design and Applications, 2010
    Co-Authors: Shyue-liang Wang, Tzung-pei Hong, Yu-chuan Tsai, Hung-yu Kao
    Abstract:

    Many approaches for preserving association rule privacy, such as association rule mining outsourcing, association rule hiding, and anonymity, have been proposed. In particular, association rule hiding on single Transaction Table has been well studied. However, hiding multi-relational association rule in data warehouses is not yet investigated. This work presents a novel algorithm to hide predictive association rules on multiple Tables. Given a target predictive item, a technique is proposed to hide multi-relational association rules containing the target item without joining the multiple Tables. Examples and analyses are given to demonstrate the efficiency of the approach.

Kamal Raj Pardasani - One of the best experts on this subject based on the ideXlab platform.

  • Mining Level-Crossing Association Rules from Large Databases
    Journal of Computer Science, 2006
    Co-Authors: Kamal Raj Pardasani
    Abstract:

    Existing algorithms for mining association rule at multiple concept level, restricted mining strong association among the concept at same level of a hierarchy. However mining level-crossing association rule at multiple concept level may lead to the discovery of mining strong association among at different level of hierarchy. In this study, a top-down progressive deepening method is developed for mining level-crossing association rules in large Transaction databases by extension of some existing multiple-level association rule mining techniques. This method is using concept of reduced support and refine the Transaction Table at each level.

Maulana Azad - One of the best experts on this subject based on the ideXlab platform.

  • Mining Level-Crossing Association Rules from Large Databases
    2006
    Co-Authors: Maulana Azad
    Abstract:

    Existing algorithms for mining association rule at multiple concept level, restricted mining strong association among the concept at same level of a hierarchy. However mining level-crossing association rule at multiple concept level may lead to the discovery of mining strong association among at different level of hierarchy. In this study, a top-down progressive deepening method is developed for mining level-crossing association rules in large Transaction databases by extension of some existing multiple-level association rule mining techniques. This method is using concept of reduced support and refine the Transaction Table at each level. (5) , clustering