Mining fuzzy sequential patterns from
quantitative transactions
指導教授：張儀興
研究生：柯常恩
教授
Outline





Introduction
Review of the AprioriAll mining approach
Review of fuzzy set concepts
Example
Conclusion
Introduction
This paper thus focuses on finding fuzzy sequential patterns from
quantitative data.A new mining algorithm is proposed, which
integrates the fuzzy-set concepts and the AprioriAll algorithm.
Review of the AprioriAll mining approach
Min_sup=0.35
Review of the AprioriAll mining approach
1
2
3
4
5
6
7
Review of fuzzy set concepts
Example
There are five items (A,B,C,D and E) in this example.Each
transaction includes a customer ID, transaction time and some
purchased items
Example
Example
Low:(6-2)/(6-1)=0.8
Middle:(2-1)/(6-1)=0.2
B.Low=0.8
B.Middle=0.2
Example
Example
Min_sup=3.0
B.Low=(0.8+0.8+1.0+0.8+0.6)=4.0
Example
B.Low,C.Middle=2.6
Example
Example
Max[min(0.4,0.8),min(0.6,0.8)]=0.6
0.6
Example
1.0
Min_sup=2.0
(D.Middle)→(B.Low),
(D.Middle, E.High)→(B.Low),
(D.Middle)→ (C.Middle).
Conclusion
In this paper, we have proposed a novel data-mining
algorithm, which can process transaction data
with quantitative values and discover interesting
sequential patterns among them