1. Web mining：a survey in the fuzzy framework
Author：Arotaritei, Dragos and Mitra, Sushmita
Source：Fuzzy Sets and Systems Volume: 148, Issue: 1, November 16, 2004, pp
Speaker：Meng-Feng Lin(林孟鋒)
Chem-Chi Huang(黃俊琦)
Date：2005/1/5

2. Outlines Introduction Web clustering Association rule mining
Web navigation
Text mining
Web personalization
Semantic web
Information retrieval
Image mining
Conclusions
Comments

3. Introduction Web Mining Broadly categorized as Web Content Mining
Web Structure Mining
Web usage Mining

4. A Web mining taxonomy

5. Web clustering (1/2)
Fuzzy c-means algorithm

6. Web clustering (2/2)
Fuzzy c-medoids(FCMdd) algorithm

7. Association rule mining(1/2)
Using inference logic form fuzzy in three phases
The equivalent fuzzy relation used for inference is expressed as

8. Association rule mining(2/2)
According to above rules .The Base case T—›T & F —› F

9. Web navigation Is categorized as Web Structure mining
The expected access rate and the required retrieval rate are expressed as fuzzy sets.
The optimal path is computedas the minimum fuzzy distance estimated between a Hurwicz opinion set.

10. Text mining
A fuzzy decision tree that uses a fuzzy inductive learning to acquire relations from examples.
The algorithm outline

11. Web personalization(1/2)
Mining typical user profiles and URL associations from the vast amount of access logs is an important component of Web personalization, that deals with tailoring a user’s interaction with the Web information space based on information about her/him.

12. Web personalization(2/2)
Nasracui
Link-based
FCoM

13. Semantic Web
A fuzzy conceptual graph (FCG) is used for knowledge representation of the Semantic Web, and is available both for human and machine processing.
Their extension to very large documents, structured and unstructured types of documents with possibly very long sentences, multimedia Web pages, etc., are subjects of ongoing investigation.

14. Information Retrieval
Fuzzy genes are used as intelligent agents for information retrieval from the Web. This incorporates a hybridization of fuzzy sets with genetic algorithms (GAs) in the soft computing framework.

15. Image Mining
Querying for a target image and retrieving it from Web and image databases, based on image similarity, is presented in . A fuzzy c-means algorithm is used to cluster intrinsic image characteristics extracted from subregions of the image.
C-means

16. Conclusions
Techniques are needed to identify only the useful/interesting patterns and present them to the user.
Fuzzy sets, which are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster.

17. Comments 經由此期刊論文中，可概略的了解到Web mining中的一個完整的架構。並且加上Fuzzy元素來增加功能性。
另外，在各功能單元中皆有譂述主要運用，但本文中若加上功能性的比較與是否可互相支援的話會更加完整。