• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Digital Library


Search: "[ keyword: Performance Evaluation ]" (13)
  1. 1. Proposal for the Hourglass-based Public Adoption-LinkedNational R&D Project Performance Evaluation Framework
  2. 2. Recent Technique Analysis, Infant Commodity Pattern Analysis Scenario and Performance Analysis of Incremental Weighted Maximal Representative Pattern Mining
    Unil Yun, Eunmi Yun, Vol. 21, No. 2, pp. 39-48, Apr. 2020
    10.7472/jksii.2020.21.2.39
    Keywords: Weighted maximal pattern mining, Incremental mining, Representative pattern, Application scenario, Performance Evaluation
  3. 3. Development of Performance Evaluation Metrics of Concurrency Control in Object-Oriented Database Systems
  4. 4. Real Time Distributed Parallel Processing to Visualize Noise Map with Big Sensor Data and GIS Data for Smart Cities
  5. 5. Analysis and Performance Evaluation of Pattern Condensing Techniques used in Representative Pattern Mining
  6. 6. Performance analysis of Frequent Itemset Mining Technique based on Transaction Weight Constraints
    Unil Yun, Gwangbum Pyun, Vol. 16, No. 1, pp. 67-74, Feb. 2015
    10.7472/jksii.2015.16.1.67
    Keywords: Transaction weight, Data Mining, Frequent Itemset mining, Performance Evaluation, scalability
  7. 7. Analysis and Evaluation of Frequent Pattern Mining Technique based on Landmark Window
    Gwangbum Pyun, Unil Yun, Vol. 15, No. 3, pp. 101-108, Jun. 2014
    10.7472/jksii.2014.15.3.101
    Keywords: Landmark Window, Frequent pattern mining, Online mining, Performance Evaluation, scalability
  8. 8. Performance Analysis of Frequent Pattern Mining with Multiple Minimum Supports
    Heungmo Ryang, Unil Yun, Vol. 14, No. 6, pp. 1-8, Dec. 2013
    10.7472/jksii.2013.14.6.01
    Keywords: Multiple minimum supports, Frequent pattern mining, Rare frequent patterns, Performance Evaluation, scalability
  9. 9. Performance evaluation of approximate frequent pattern mining based on probabilistic technique
  10. 10. A Comparison of Performance between STMP/MST and Existing Spatio-Temporal Moving Pattern Mining Methods