We try to explore category and association rules of customer questions by applying customer analysis and the combination of data mining and rough set theory

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Clustering Analysis Data

Mining Rough Set Theory

Dynamic Frequently Asked Questions FAQ

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Abstract

Nowadays the enterprise has regarded the customer relationship management as one of management successful factors, what an enterprise focuses on is customer-oriented service. Therefore the foundation for an enterprise to keep good customer relationship is to listen and solve customer’s questions. With the popularization of network development, the network became the main communication channel between enterprise and customers. Also, customer questions inquired directly through network day by day. Therefore the demand to establishes a mechanism that can automatically respond to customer questions gradually draws attention of the enterprise.

In this research, we first break the customer questions by words processing analysis. Then draw representative keywords with characteristics. We try to explore category and association rules of customer questions by applying customer analysis and the combination of data mining and rough set theory. We use customer questions association intensity to establish a dynamic frequently asked questions (FAQ).

To improve how an enterprise used to construct FAQ from the viewpoint of the enterprise, in this research, we apply actual customer questions from a company as the

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user interaction, we analyze information of the user feedback to achieve a FAQ with learning ability. The contribution of this research is to assist the enterprise to cut down cost to solve the customer common questions, and then to promote good relationship between an enterprise and customers. The purpose is to achieve customer oriented service from the customer’s viewpoint.

Keywords: Frequently Asked Question (FAQ) Data Mining Cluster Analysis Rough Set Theory

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B CKIP ………..98

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Recall ………..………69

Accuracy ……….………70

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Precision Recall Accuracy …….66

Precision ……….………68

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Accuracy ……….70

Precision Recall Accuracy ………..70

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[Walton O. Anderson, Jr, 2001]

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FAQ

FAQ [ 2003] FAQ

FAQ FAQ

Question & Answer, Q&A

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FAQ FAQ

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FAQ Customer Questions Oriented FAQ FAQ

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FAQ Cluster Analysis Rough Set Theory

FAQ

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FAQ

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FAQ FAQ

FAQ

Network World 2000 Call Center

[

2001] FAQ FAQ Finder Ask Jeeves

FAQ FAQ FAQ

FAQ Finder[Robin D. Burke, Kristian J. Hammond, Steven L. Lytinen,

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FAQ

FAQ Finder FAQ

FAQ Finder

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Term Frequency IDF Inverse Document Frequency TF-IDF FAQ

2. Semantic Princeton University

Word Net FAQ

3. Coverage

FAQ FAQ

4.

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FAQ Ask Jeeves

FAQ Ask Jeeves

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FAQ FAQ

FAQ

2000 FAQ

FAQ 2001

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2000 FAQ

[Michael J. A. Berry;

Gorden S.Linoff, 2001]

Knowledge Discovery [Fayyad, U., Gregory, P.S., Padhraic, S,

1996] [Jiawei Han , Micheline

Kamber,2000 ][ Margaret H. Dunham, 2002]

1. Data Cleaning and Integration

Noise

2. Data Selection

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3. Data Transformation

4. Data Mining

5. Pattern Evaluation

Support Value Confidence Value

6. Knowledge Presentation Visualization

Data Selection

Data Transformation

Data Mining

Pattern Evaluation

Knowledge Presentation

Data Base Data Integration Data Cleaning

Data

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Classification Estimation Prediction Affinity Grouping Clustering Analysis [C. Westphal; T. Blaxton, 1998]

1.

Class Description Supervised Learning

2. Class

Discrete

3.

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Time Sequence Analysis

4.

Market Basket Analysis [Cunningham, S.J.; Frank, E., 2001]

Association Rule [R. Agrawal; T. Imielinski; A. Swami, 1993][Kitty S. Y. Chiu, Rober W. P. Luk, Keith C. C. Chan and Korris F. L. Chung, 2002]

Cross Selling

5.

Cluster

K-means

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( )

Cluster Criterion Function

Dissimilarity

[ 2002]

[ 2001]

[ 2001]

FAQ

(27)

( )

Hierarchical Clustering Nonhierarchical Clustering [ 2000]

[ 2000]

Single Link Complete Link

Average Link K-means

[Z. Huang, 1997][J. Han and M. Kamber, 2001]

Agglomerative Clustering Methods

Button-up

Inter-cluster Similarity

Minimum Distance [ ,2000]

Ci Cj

Ci Cj

(28)

Average Distance

O n2

O n3 Chaining effect

1.

[ 2000]

2.

3. O n3

1.

O n2

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2.

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Supervised Learning Unsupervised Learning

[Steinbach, Karypis and Kumar,2000]

Hierarchical Clustering [Zhao and Karypis,2002] Refinement Clustering [Pantel and Lin,2002]

Fuzzy Means

[Baker and McCallum,1998]

Mean Method [ 2003]

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[Lin and Kondadadi,2001]

[ 2003] Steinbach 2000 Bisecting K-Means

Algorithm K K-Means

Zhao Karypis 2002

K K

[ 2003]

K-Nearest Neighbor 2000

2002 2001

2002

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Rough Set Theory

Rough Set Theory Z.Pawlak 1982

Rough Set

1.

2.

3.

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(32)

6.

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Classification

/2000

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Chiam/2000

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and A.

He/2000

A Rough Set Approach in Choosing Partitioning

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/2002

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Du, D.

Yamada and N. Ishii/2002

A Rough Set-Based Hybrid Method to Text Categorization

Latent Semantic Indexing, LSI

S.C. Lee and M.J.

Huang/2002

Applying AI Technology and Rough Set Theory for

Mining Association Rules to Support Crime Management

and Fire-Fighting Resources Allocation

(Self- Organization Map, SOM)

/2003

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Approximation Lower / Upper Approximations of a Set

Decision Equivalent Classes Condition Equivalent Classes

[Duntsch, I. And Gediga, G., 1999]

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Soft Computing

AI [Ivo and Gunther,2000]

[Munakata, T., 1998]

Microscopic, Primarily Numeric Macroscopic, Descriptive and Numeric Deductive Chaos Theory Fuzzy methods

Inductive Neural Networks, Genetic Algorithms

Rough Set Data Analysis

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FAQ

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Lin Kondadadi 2001

Concept Hierarchy

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(45)

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3

(VJ) (D) (P) (Nc) (VJ)

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3

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0

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5 5

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(49)

Lin Knodadadi 2001

1.

(50)

2.

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5 5

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Concept Hierarchy

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(53)
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Qk Record Qk N Qs

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7 K TERMS_ FAQ

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Y X Y X Y

X Y

Similarity Measure Definition

Cosine Coefficient X Y

X Y

Jaccard Coefficient X Y

X Y

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1 ( x1, x2,... xn) Q tf tf tf

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2002 3 29 2003 11 5 2003 12 1 2004

3 17 1243 A

A

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1.

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4.

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422

821 521

300

521

43

-1 -1

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(69)

-1 -2 -3

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Q01_0000 357

Q01_1000 128

Q01_1010 35

Q01_1020 29

Q01_1030 23

Q01_1040 18

Q01_1050 16

Q01_1060 7

Q01_1060_1 4

Q01_1060_2 3

Q01_2000 83

Q01_2010 36

Q01_2020 31

(70)

Q01_2030_1 19

Q01_2030_2 12

Q01_2030_3 5

Q01_3000 53

Q01_3010 23

Q01_3020 19

Q01_3030 6

Q01_3040 5

Q01_4000 25

Q01_4010 > 10

Q01_4020 6

Q01_4030 9

Q01_5000 21

Q01_6000 47

Q01_6010 14

Q01_6010_1 8

Q01_6010_2 6

Q01_6020 13

Q01_6030 6

Q02_0000 164

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Q02_1010 41

Q02_1020 32

Q02_1030 21

Q02_1040 17

Q02_1050 53

Q02_1050_1 25

Q02_1050_2 17

Q02_1050_3 11

Precision Recall Accuracy

1.

300

(72)

2.

Q01_1020 17

283 Q01_1020

17 1

Q01_1020 16

282 1

_

SysQue Cmatch _

SysQue Nmatch

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SysQue Fmatch

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ManQue Cmatch _

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(ManQue Cmatch_ ManQue Nmatch_ )

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Correct Ratio False Ratio Miss Ratio

Correct Ratio _ _

_ _

SysQue Cmatch SysQue Nmatch ManQue Cmatch ManQue Nmatch

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_

_

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Miss Ratio _ _

_

ManQue Cmatch SysQue Cmatch ManQue Cmatch

3.

300

[ ]/A Dj 0.73 Upper

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Q01_0000 239 61 223 56 5 93.00% 8.197% 6.695%

Q01_1000 83 217 71 211 6 94.00% 2.765% 14.458%

Q01_1010 24 276 21 264 12 95.00% 4.348% 12.500%

Q01_1020 17 283 14 271 12 95.00% 4.240% 17.647%

Q01_1030 16 284 16 273 11 96.33% 3.873% 0.000%

Q01_1040 5 295 5 280 15 95.00% 5.085% 0.000%

Q01_1050 6 294 5 288 6 97.67% 2.041% 16.667%

Q01_1060 7 293 6 280 13 95.33% 4.437% 14.286%

Q01_1060_1 5 295 5 281 14 95.33% 4.746% 0.000%

Q01_1060_2 3 297 3 276 21 93.00% 7.071% 0.000%

Q01_2000 64 236 54 229 7 94.33% 2.966% 15.625%

Q01_2010 13 287 12 278 9 96.67% 3.136% 7.692%

Q01_2020 9 291 9 276 15 95.00% 5.155% 0.000%

Q01_2030 12 288 11 274 14 95.00% 4.861% 8.333%

Q01_2030_1 13 287 10 274 13 94.67% 4.530% 23.077%

Q01_2030_2 8 292 8 282 10 96.67% 3.425% 0.000%

Q01_2030_3 9 291 8 275 16 94.33% 5.498% 11.111%

Q01_3000 33 267 26 257 10 94.33% 3.745% 21.212%

Q01_3010 16 284 14 274 10 96.00% 3.521% 12.500%

Q01_3020 8 292 7 279 13 95.33% 4.452% 12.500%

Q01_3030 5 295 5 283 12 96.00% 4.068% 0.000%

Q01_3040 4 296 4 278 18 94.00% 6.081% 0.000%

Q01_4000 14 286 14 281 5 98.33% 1.748% 0.000%

(75)

Q01_4010 3 297 3 279 18 94.00% 6.061% 0.000%

Q01_4020 5 295 5 281 14 95.33% 4.746% 0.000%

Q01_4030 6 294 6 283 11 96.33% 3.741% 0.000%

Q01_5000 11 289 10 280 9 96.67% 3.114% 9.091%

Q01_6000 22 278 18 271 7 96.33% 2.518% 18.182%

Q01_6010 11 289 9 276 11 95.00% 3.806% 18.182%

Q01_6010_1 6 294 6 281 13 95.67% 4.422% 0.000%

Q01_6010_2 5 295 5 286 9 97.00% 3.051% 0.000%

Q01_6020 8 292 7 280 12 95.67% 4.110% 12.500%

Q01_6030 4 296 4 287 9 97.00% 3.041% 0.000%

Q02_0000 61 239 53 233 6 95.33% 2.510% 13.115%

Q02_1000 47 253 40 244 9 94.67% 3.557% 14.894%

Q02_1010 14 286 13 272 14 95.00% 4.895% 7.143%

Q02_1020 8 292 7 281 11 96.00% 3.767% 12.500%

Q02_1030 6 294 6 280 14 95.33% 4.762% 0.000%

Q02_1040 5 295 5 280 15 95.00% 5.085% 0.000%

Q02_1050 14 286 13 277 9 96.67% 3.147% 7.143%

Q02_1050_1 8 292 7 281 11 96.00% 3.767% 12.500%

Q02_1050_2 3 297 3 287 10 96.67% 3.367% 0.000%

Q02_1050_3 3 297 3 288 9 97.00% 3.030% 0.000%

93.00% 95.52% 8.197% 4.104% 15.385% 7.4314%

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1

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521

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1.

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Precision Recall Accuracy

Precision Recall

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Precision Recall Accuracy

Precision Recall Accuracy

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Precision Recall Accuracy

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Precision Recall Accuracy

1 Precision

(80)

Precision Precision

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1 173

288

Precision

2 Recall

(81)

Recall Recall

0.11 0.19 2 0.21 0.29 1 0.31 0.39 4 0.41 0.49 3 0.51 0.59 9 0.61 0.69 4 0.71 0.79 25 0.81 0.89 38 0.91 0.99 57

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Recall

3 Accuracy

(82)

Accuracy

Accuracy Accuracy

0.81 0.91 6

0.82 0.92 6

0.83 0.93 7

0.84 0.94 11

0.85 0.95 10

0.86 1 0.96 13

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Accuracy

Precision Recall Accuracy Precision Recall Accuracy

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300

288 A

1 Precision 1 173 300

173 Precision 93.31%

2 Recall

Recall

Recall

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3 Accuracy 97.53%

(84)

FAQ

Accuracy 1

Accuracy 1

(85)
(86)

2003

(87)

1.

50 2.

A

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2.

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(89)

Intention

(90)

1 2000

2 2000 FAQ

3 2000 FAQ

4 2000

5 2001 FAQ

6 2002

7 2003

8 2002

9 2000

(91)

10 2003

11 2002

12 2003

13 CKIP

http://godel.iis.sinica.edu.tw/CKIP/ws/

14 1993

1 A. Chouchoulas and Q. Shen. (1999), “A Rough Set Approach to Text Classification”, In Proc. of the 7th International Workshop on Rough Sets, Page: 118-127.

2 C. Westphal and T. Blaxton (1998), “Data Mining Solutions-Methods and Tools for Solving Real-World Problems”, John Wiley & Sons.

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