Conclusion and future work

In this paper, we have proposed a new weighted web-mining algorithm, which can

process web-server logs to discover useful sequential browsing patterns with linguistic

supports. The web pages are evaluated by managers as linguistic terms, which are then

transformed and averaged as fuzzy sets of weights. Fuzzy operations including fuzzy ranking

are used to find weighted sequential browsing patterns. Compared to previous mining

approaches, the proposed one has linguistic inputs and outputs, which are more natural and

understandable for human beings.

Although the proposed method works well in weighted web mining from log data, and

can effectively manage linguistic minimum supports, it is just a beginning. There is still much

work to be done in this field. Our method assumes that the membership functions are known

in advance. In [20-21, 23], we proposed some fuzzy learning methods to automatically derive

the membership functions. In the future, we will attempt to dynamically adjust the

membership functions in the proposed web-mining algorithm to avoid the bottleneck of

membership function acquisition.

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