Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by Shyue-Liang Wang
Total Records ( 2 ) for Shyue-Liang Wang
  Tzung-Pei Hong , Ming-Jer Chiang and Shyue-Liang Wang
  World-wide-web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has become a very important research topic in this field. Techniques of web mining have thus been requested and developed to achieve this purpose. In this research, a new fuzzy weighted web-mining algorithm is proposed, which can process web-server logs to discover useful users’ browsing behaviors from the time durations of the paged browsed. Since the time durations are numeric, fuzzy concepts are used here to process them and to form linguistic terms. Besides, different web pages may have different importance. The importance of web pages are evaluated by managers as linguistic terms, which are then transformed and averaged as fuzzy sets of weights. Each linguistic term is then weighted by the importance for its page. Only the linguistic term with the maximum cardinality for a page is chosen in later mining processes, thus reducing the time complexity. The minimum support is set linguistic, which is more natural and understandable for human beings. An example is given to clearly illustrate the proposed approach.
  Tzung-Pei Hong , Ya-Fang Tung , Shyue-Liang Wang , Min-Thai Wu and Yu-Lung Wu
 

Data mining is often used to find out interesting and meaningful patterns from huge databases. It may generate different kinds of knowledge such as classification rules, clusters, association rules, and among others. A lot of researches have been proposed about data mining and most of them focused on mining from binary-valued data. Fuzzy data mining was thus proposed to discover fuzzy knowledge from linguistic or quantitative data. Recently, ant colony systems (ACS) have been successfully applied to optimization problems. However, few works have been done on applying ACS to fuzzy data mining. This thesis thus attempts to propose an ACS-based framework for fuzzy data mining. In the framework, the membership functions are first encoded into binary-bits and then fed into the ACS to search for the optimal set of membership functions. The problem is then transformed into a multi-stage graph, with each route representing a possible set of membership functions. When the termination condition is reached, the best membership function set (with the highest fitness value) can then be used to mine fuzzy association rules from a database. At last, experiments are made to make a comparison with other approaches and show the performance of the proposed framework.

 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility