Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Author: TZUNG-PEI HONG KUEI-YING LIN BEEN-CHIAN CHIEN

Advisor: Dr. HsuGraduate: Yan Pin Huang

ADSL Wednesday, October 01, 2003

Content

Motivation Objective Introduction Notation The Multiple-Level Fuzzy Data-Mining Algorithm Experimental Results Conclusion Personal opinion

Motivation

Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge.

Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level.

Objective

This paper proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values.

The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets.

It integrates fuzzy-set concepts, data-mining technologies and multiple-level taxonomy to find fuzzy association rules from transaction data sets.

Introduction

Proposed a method for mining association rules from data sets using quantita-tive and categorical attributes. R. Srikant and R. Agrawal, “Mining quantitative as

sociation rules in large relational tables,” in The 1996 ACM SIGMOD International Conference on Management of Data.

Introduction(cont.)

Fuzzy set theory is being used more and more fre-quently in intelligent systems because of its simplicity and similarity to human reasoning [15].

Mining at Multiple Concept Levels They divided the min-ing process into two ph

ases. Candidate itemsets were generated and counted

by scanning the transaction data. Association rules were induced from the large ite

msets found in the first phase

Mining at Multiple Concept Levels

Notation(cont.)

Notation(cont.)

The Multiple-Level FuzzyData-Mining Algorithm

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 1. Each item

name is first encoded using the predefined taxonomy. Results are shown in Table 2.

The Multiple-Level FuzzyData-Mining Algorithm(cont)

1**

11*

111

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 2. All transactions shown in Table 1 are then

encoded using the above coding scheme Step 3. k is initially set at 1, where k is used to store the

level number being processed. Step 4. All the items in the transactions are first

grouped on level one.

The Multiple-Level FuzzyData-Mining Algorithm(cont)

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 5. The quantitative values of the items on level

1 are represented using fuzzy sets.

The Multiple-Level FuzzyData-Mining Algorithm(cont) (1,1)(6,0) 帶入 y=ax+b 求解 a,b (a=-1/5 b=6/5) Function: y= -1/5x+6/5 (1**,5) 帶入 member function 求得 y=0.2

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 6. The scalar

cardinality of each fuzzy region in the transactions is calculated as the count value.

Its scalar cardinality=(0.8+0.8+ 0.0+0.2+0.0+0.0) =1.8

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 7. The fuzzy

region with the highest count among the three possible regions for each item is found.

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 8. The count of any region selected in Step 7 is

checked against the predefined minimum support value α. (α =2.1)

The Multiple-Level FuzzyData-Mining Algorithm(cont)

1.2

The Multiple-Level FuzzyData-Mining Algorithm(cont)

Step 12. r is set at 2, where r is used to store the number of items kept in the current itemsets.

Step 13. Since is null, k =k + 1=2 and Step 4 is done. The results for level 2 are shown in Table 10.The results for level 3 are shown in Table 11.Since there are no items on level 4, Step 17 is done.

The Multiple-Level FuzzyData-Mining Algorithm(cont)

Step 17. The association rules are constructed for each large itemset using the following substeps.

The Multiple-Level FuzzyData-Mining Algorithm(cont)

The Multiple-Level FuzzyData-Mining Algorithm(cont) Step 18. The confidence values of the possible

association rules are checked against the predefined confidence threshold λ. (λ =0.7)

6. Experimental ResultsThey were implemented in C on a Pentium-III 700 Personal Computer.The number of levels was set at 3. 64 purchased items (terminal nodes) on level 3, 16generalized items on level 2, and 4 generalized items on level 1.

Experimental Results(cont)

Experimental Results(cont)

Discussion and Conclusions

Proposed a fuzzy multiplelevel data-mining algorithm that can process transaction data with quantitative values and discover interesting patterns among them.

This method achieves better time complexity since only the most important fuzzy term is used for each item.

This proposed algorithm does not find association rules for items on the same paths in given hierarchy trees.

Discussion and Conclusions

We will therefore attempt to dynamically adjust the membership functions in the proposed mining algorithm

We will also attempt to design specific data-mining models for various problem domains.

Personal opinion Find association rules for items on the same paths in

given hierarchy trees. Find a method that can dynamically adjust the

membership functions. Fuzzy SOM. Fuzzy clustering. The strategy of using fuzzy set in Ant Colony

algorithm.

Personal opinion(cont.)

Personal opinion(cont.)