Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

1,710 views

1,630 views

1,630 views

Published on

Published in:
Education

No Downloads

Total views

1,710

On SlideShare

0

From Embeds

0

Number of Embeds

15

Shares

0

Downloads

0

Comments

0

Likes

7

No embeds

No notes for slide

- 1. Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms Jesús Alcalá-Fdez, Rafael Alcalá María José Gacto, Francisco Herrera Fuzzy Sets and Systems (2008), article in press Presenter: Chia-Ming Wang
- 2. Before we go Thanks to Prof. Hong who provide me the second paper today.
- 3. Before we go • T. Hong, C. Chen,Y. Wu,Y. Lee, Using divide- and-conquer GA strategy in fuzzy data mining, in: IEEE Symp. on Fuzzy Systems, Budapest, Hungary, 2004, pp. 116–121. • T. Hong, C. Kuo, S. Chi,Trade-off between time complexity and number of rules for fuzzy mining from quantitative data, Journal of Uncertain Fuzziness Knowledge-Based Systems 9 (5) (2001) 587–604. Thanks to Prof. Hong who provide me the second paper today.
- 4. Problem Description 2-tuples Quantitative GA model Association Rule
- 5. A Transaction Database TID items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 6. Association Rule Mining Examples: TID items {Diaper}→{Beer} 1 Bread, Milk {Milk, Bread}→{Eggs, coke} 2 Bread, Diaper, Beer, Eggs {Beer, Bread}→{Milk} 3 Milk, Diaper, Beer, Coke X→Y, X∩Y=∅ 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 7. Association Rule Mining Examples: TID items {Diaper}→{Beer} 1 Bread, Milk {Milk, Bread}→{Eggs, coke} 2 Bread, Diaper, Beer, Eggs {Beer, Bread}→{Milk} 3 Milk, Diaper, Beer, Coke X→Y, X∩Y=∅ 4 Bread, Milk, Diaper, Beer Implication means co-occurrence, 5 Bread, Milk, Diaper, Coke not causality!
- 8. Terminology Examples: {Milk, Diaper}→{Beer} TID items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 9. Terminology Examples: {Milk, Diaper}→{Beer} TID items support 1 Bread, Milk σ{Milk, Diaper, Beer} 2 s= = = 0.4 2 Bread, Diaper, Beer, Eggs |T| 5 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 10. Terminology Examples: {Milk, Diaper}→{Beer} TID items support 1 Bread, Milk σ{Milk, Diaper, Beer} 2 s= = = 0.4 2 Bread, Diaper, Beer, Eggs |T| 5 3 Milk, Diaper, Beer, Coke conﬁdent 4 Bread, Milk, Diaper, Beer c= σ{Milk, Diaper, Beer} 2 = = 0.67 σ{Milk, Diaper} 3 5 Bread, Milk, Diaper, Coke
- 11. Terminology Examples: {Milk, Diaper}→{Beer} TID items support 1 Bread, Milk σ{Milk, Diaper, Beer} 2 s= = = 0.4 2 Bread, Diaper, Beer, Eggs |T| 5 3 Milk, Diaper, Beer, Coke conﬁdent 4 Bread, Milk, Diaper, Beer c= σ{Milk, Diaper, Beer} 2 = = 0.67 σ{Milk, Diaper} 3 5 Bread, Milk, Diaper, Coke Itemset, minsup, minconf
- 12. Real-world Transaction Database TID (item, quantity) 1 (Bread, 3), (Milk, 1) 2 (Bread, 1), (Diaper, 2), (Beer, 3), (Eggs, 12) 3 (Milk,2), (Diaper, 4), (Beer, 5), (Coke, 2) 4 (Bread, 3), (Milk, 1), (Diaper, 2), (Beer, 12) 5 (Bread, 2), (Milk, 4), (Diaper, 5), (Coke, 3)
- 13. Real-world Transaction Database TID (item, quantity) 1 (Bread, 3), (Milk, 1) 2 Quantitative 3), (Eggs, 12) (Bread, 1), (Diaper, 2), (Beer, Association Rule 3 (Milk,2), (Diaper, 4), (Beer, 5), (Coke, 2) Mining 4 (Bread, 3), (Milk, 1), (Diaper, 2), (Beer, 12) 5 (Bread, 2), (Milk, 4), (Diaper, 5), (Coke, 3)
- 14. Quantitative Association Rule
- 15. 2-tuples Quantitative model Association Rule
- 16. Linguistic terms Low Middle High Low Middle High age weight if age is Middle then weight is High
- 17. The 2-tuples linguistic representation if age is Middle then weight is High F. Herrera, L. Martínez, A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Trans. Fuzzy Systems 8 (6) (2000) 746–752.
- 18. The 2-tuples linguistic representation if age is Middle then weight is High if age is (Middle, 0.3) then weight is (High, -0.1) (si , αi ), si ∈ S, αi ∈ [−0.5, 0.5) F. Herrera, L. Martínez, A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Trans. Fuzzy Systems 8 (6) (2000) 746–752.
- 19. -1 -0.5 0.5 1 s0 s1 s2 s3 s4 domain 0 1 2 3 4 (s2, -0.3)
- 20. -1 -0.5 0.5 1 s0 s1 s2 s3 s4 -0.3 domain 1.7 0 1 2 3 4 (s2, -0.3)
- 21. -1 -0.5 0.5 1 s0 s1 s2 s3 s4 -0.3 domain 1.7 0 1 2 3 4 (s2, -0.3) -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 s0 s1 s2 s3 s4 0 1 2 3 4
- 22. -1 -0.5 0.5 1 s0 s1 s2 s3 s4 -0.3 domain 1.7 0 1 2 3 4 (s2, -0.3) -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 α=-0.3 s0 s1 s2 s3 s4 (s2, -0.3) 0 1 2 3 4
- 23. Interpretation if age is (Middle, 0.3) then weight is (High, -0.1)
- 24. Interpretation if age is (Middle, 0.3) then weight is (High, -0.1) if age is (higher than Middle) then weight is (a bit smaller than High)
- 25. 2-tuples model
- 26. 2-tuples GA model
- 27. Traditional GA
- 28. Traditional GA Population (chromosomes)
- 29. Traditional GA Population (chromosomes) parents Evaluation (ﬁtness)
- 30. Traditional GA Population (chromosomes) parents Evaluation (ﬁtness) Reproduction Mating pool (selection)
- 31. Traditional GA Population (chromosomes) parents ‣ crossover Genetic Evaluation ‣ mutation operators (ﬁtness) Mates Reproduction Mating pool (recombination) (selection)
- 32. Traditional GA Population (chromosomes) offsprings parents ‣ crossover Genetic Evaluation ‣ mutation operators (ﬁtness) Mates Reproduction Mating pool (recombination) (selection)
- 33. GA Used in this paper • CHC genetic model • MFs codiﬁcation and initial gene pool • Chromosome evaluation • Crossover operator
- 34. GA Used in this paper • CHC genetic model • MFs codiﬁcation and initial gene pool • Chromosome evaluation • Crossover operator
- 35. Scheme of CHC model L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 36. Scheme of CHC model Initialize population and THRESHOLD L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 37. Scheme of CHC model Initialize population Crossover of N and THRESHOLD parents L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 38. Scheme of CHC model Initialize population Crossover of N and THRESHOLD parents Incest prevention 1/2 * hamming distance > L L = (#Genes *BITSGENE)/4 L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 39. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 40. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals Selection of the best N individuals between parents and offsprings L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 41. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals Selection of the best N individuals between parents and offsprings if NO new individual, decrement THRESHOLD L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 42. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals Selection of the best N individuals between parents and offsprings THRESHOLD if NO new individual, <0 decrement THRESHOLD L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 43. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals Selection of the best N individuals between parents and offsprings no THRESHOLD if NO new individual, <0 decrement THRESHOLD L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 44. Scheme of CHC model Initialize population Crossover of N Evaluation of the and THRESHOLD parents New Individuals Selection of the best N individuals between parents and offsprings no Restart the population THRESHOLD if NO new individual, and THRESHOLD <0 decrement THRESHOLD yes L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283.
- 45. GA Used in this paper • CHC genetic model • MFs codiﬁcation and initial gene pool • Chromosome evaluation • Crossover operator
- 46. age L1 M1 H1 L2 M2 H2 weight L1 M1 H1 L2 M2 H2 0 0 0 0 0 0 MFs Codiﬁcation
- 47. age L1 M1 H1 L2 M2 H2 weight L1 M1 H1 L2 M2 H2 0 0 0 0 0 0 MFs Codiﬁcation L1 M1 H1 L2 M2 H2 0.2 0.4 0 -0.2 -0.3 -0.5 age L1 M1 H1 L2 M2 H2 weight
- 48. Initial Gene Pool chromosome: (c11,...,c1m,c21,...,c2m,...,cn1,...,cnm) 1 item with m MFs • initial MFs obtained from expert knowledge • individuals generated at random in [-0.5, 0.5)
- 49. Implementation: Gray Code Decimal Binary Gray Code 0 000 000 1 001 001 2 010 011 3 011 010 4 100 110 5 101 111 6 110 101 7 111 100
- 50. Implementation: Gray Code Decimal Binary Gray Code 0 000 000 1 001 001 2 010 011 3 011 010 4 100 110 5 101 111 6 110 101 7 111 100
- 51. GA Used in this paper • CHC genetic model • MFs codiﬁcation and initial gene pool • Chromosome evaluation • Crossover operator
- 52. Equation Mania x∈L1 f uzzy support f itness(Cq ) = suitability(Cq ) n suitability(Cq ) = [overlap f actor(Cqk ) + coverage f actor(Cqk )] k=1 m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) n suitability(Cq ) = [overlap f actor(Cqk ) + 1] k=1
- 53. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi )
- 54. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item
- 55. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj
- 56. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj overlap
- 57. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj overlap SpanR
- 58. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj overlap SpanR SpanL
- 59. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj Ri Rj overlap overlap SpanR SpanR SpanL SpanL
- 60. m m overlap(Ri , Rj ) overlap f actor(Cqk ) = [max( , 1) − 1] i=1 j=i+1 min(spanRRi , spanLRi ) qth chromosome kth item Ri Rj Ri Rj penalty overlap overlap SpanR SpanR SpanL SpanL
- 61. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik )
- 62. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) qth chromosome kth item
- 63. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) qth chromosome kth item R1 R2 R3 Milk 0 5 10
- 64. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) qth chromosome kth item R1 R2 R3 R1 R2 R3 Milk Milk 0 5 10 0 5 10
- 65. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) qth chromosome kth item R1 R2 R3 R1 R2 R3 Milk Milk 0 5 10 0 5 10 range
- 66. 1 coverage f actor(Cqk )= range(R 1 ,...,Rm ) max(Ik ) qth chromosome kth item R1 R2 R3 R1 R2 R3 Milk Milk 0 5 10 0 5 10 coverage f actor(Cqk ) = 1 range
- 67. Fuzzy Support (count)
- 68. Fuzzy Support (count) DB n item T
- 69. Fuzzy Support (count) DB n item (i) vj T ith
- 70. Fuzzy Support (count) DB n item (i) vj T ith (i) (i) (i) fj1 fjm bread fj = + ··· Rj1 Rjm
- 71. Fuzzy Support (count) DB n item (i) vj T ith (i) (i) (i) fj1 fjm bread fj = + ··· Rj1 Rjm item m mf
- 72. Fuzzy Support (count) DB n item (i) vj degree T ith (i) (i) (i) fj1 fjm bread fj = + ··· Rj1 Rjm item m mf
- 73. Fuzzy Support (count) DB n item (i) vj degree T ith (i) (i) (i) fj1 fjm bread fj = + ··· Rj1 Rjm T (i) countjk = fjk item i=1 m mf bread.low.count
- 74. Fuzzy Support (count) DB n item (i) vj degree T ith (i) (i) (i) fj1 fjm bread fj = + ··· Rj1 Rjm T (i) countjk = fjk item i=1 m mf bread.low.count L1 = {Rjk |countjk ≥ α, 1 ≤ j ≤ n and 1 ≤ k ≤ m n item
- 75. Fuzzy Support x∈L1 f uzzy support f itness(Cq ) = suitability(Cq )
- 76. Fuzzy Support x∈L1 f uzzy support f itness(Cq ) = suitability(Cq ) n suitability(Cq ) = [overlap f actor(Cqk ) + 1] k=1
- 77. Fuzzy Support x∈L1 f uzzy support f itness(Cq ) = suitability(Cq ) n suitability(Cq ) = [overlap f actor(Cqk ) + 1] k=1 n item
- 78. Fuzzy Support L1 x∈L1 f uzzy support f itness(Cq ) = suitability(Cq ) n suitability(Cq ) = [overlap f actor(Cqk ) + 1] k=1 n item
- 79. Fuzzy Support L1 count / T # transaction x∈L1 f uzzy support f itness(Cq ) = suitability(Cq ) n suitability(Cq ) = [overlap f actor(Cqk ) + 1] k=1 n item
- 80. GA Used in this paper • CHC genetic model • MFs codiﬁcation and initial gene pool • Chromosome evaluation • Crossover operator
- 81. PCBLX Crossover X = (x1 · · · xn ) Y = (y1 · · · yn ) (xi , yi ∈ [ai , bi ] ⊂ R, i = 1 · · · n) O1 = (o11 · · · o1n ) [li , u1 ] li = max{ai , xi − Ii · α} u2 = min{bi , xi + Ii · α} 1 i 1 i O2 = (o21 · · · o2n ) [li , u2 ] li = max{ai , yi − Ii · α} u2 = min{bi , yi + Ii · α} 2 i 2 i Ii = |xi − yi | F. Herrera, M. Lozano, A.M. Sánchez, A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int. J. Intell. Syst. 18 (2003) 309-338.
- 82. PCBLX Crossover X = (x1 · · · xn ) Y = (y1 · · · yn ) (xi , yi ∈ [ai , bi ] ⊂ R, i = 1 · · · n) O1 = (o11 · · · o1n ) [li , u1 ] li = max{ai , xi − Ii · α} u2 = min{bi , xi + Ii · α} 1 i 1 i O2 = (o21 · · · o2n ) [li , u2 ] li = max{ai , yi − Ii · α} u2 = min{bi , yi + Ii · α} 2 i 2 i Ii = |xi − yi | ai xi yi bi PCBLX BLX F. Herrera, M. Lozano, A.M. Sánchez, A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int. J. Intell. Syst. 18 (2003) 309-338.
- 83. Conceptual Flowchart
- 84. Conceptual Flowchart Learning Membership Function
- 85. Conceptual Flowchart Learning Membership Function Learning Process Predeﬁned MFs Transaction Database
- 86. Conceptual Flowchart Learning Membership Function Learning Process Predeﬁned MFs Evaluation Module (Fitness) Transaction Database MFs
- 87. Conceptual Flowchart Learning Mining Fuzzy Membership Function Association Rules Learning Process Predeﬁned MFs Evaluation Module (Fitness) Transaction Database MFs
- 88. Conceptual Flowchart Learning Mining Fuzzy Membership Function Association Rules Learning Fuzzy Process mining Predeﬁned MFs Deﬁnitive MFs Evaluation Module (Fitness) Transaction Transaction Database Database MFs
- 89. Conceptual Flowchart Learning Mining Fuzzy Membership Function Association Rules Learning Fuzzy Process mining Predeﬁned MFs Deﬁnitive MFs Evaluation Module (Fitness) Transaction Transaction Database Database Fuzzy Association Rules MFs
- 90. Procedures Stage 1 1. initialization 2. evaluate the initial chromosomes 1. for all items in transaction, transfer the quantitative values to fuzzy sets 2. calculate count, fuzzy support 3. calculate ﬁtness 3. set threshold L 4. generate the next population 5. CHC procedure 6. if # run not reach, goto step4 Stage 2 Mining Fuzzy association rules by (Hong 2001)
- 91. Experiments
- 92. Parameters Proposed Hong’s • # 50 individuals • 0.01 mutation rate • 10,000 evaluations • 0.35 d factor • 30 bits per gene • 0.6 crossover rate • 0.8 fuzzy rule conﬁdent
- 93. Data Set Bureau of the Census FAM95 #63,756 instance #23 attr. #10 attr. This data set was obtained from the Statistics Data Sets Archive website http://www.stat.ucla.edu/data/fpp.
- 94. Results obtained in the genetic process Proposed approach Hong el al.’s approach Uniform fuzzy partition Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I With three linguistic terms 0.2 0.99 11.68 11.85 20 0.2 0.68 10.83 15.83 19 0.2 0.92 9.24 10.00 16 0.5 0.94 11.68 12.39 17 0.5 0.53 10.28 19.45 15 0.5 0.76 7.55 10.00 10 0.7 0.66 6.98 10.63 9 0.7 0.37 6.55 17.94 8 0.7 0.57 5.71 10.00 7 0.9 0.28 2.80 10.00 3 0.9 0.00 0.00 14.75 0 0.9 0.00 0.00 10.00 0 With ﬁve linguistic terms 0.2 0.95 10.46 10.99 22 0.2 0.53 10.22 19.27 22 0.2 0.94 9.43 10.00 21 0.5 0.77 9.92 12.92 15 0.5 0.38 7.95 20.63 12 0.5 0.46 4.57 10.00 7 0.7 0.61 7.69 12.57 10 0.7 0.20 3.96 19.54 5 0.7 0.24 2.36 10.00 3 0.9 0.10 0.92 10.00 1 0.9 0.06 0.90 15.01 1 0.9 0.00 0.00 10.00 0
- 95. Results obtained in the genetic process Proposed approach Hong el al.’s approach Uniform fuzzy partition Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I With three linguistic terms 0.2 0.99 11.68 11.85 20 0.2 0.68 10.83 15.83 19 0.2 0.92 9.24 10.00 16 0.5 0.94 11.68 12.39 17 0.5 0.53 10.28 19.45 15 0.5 0.76 7.55 10.00 10 0.7 0.66 6.98 10.63 9 0.7 0.37 6.55 17.94 8 0.7 0.57 5.71 10.00 7 0.9 0.28 2.80 10.00 3 0.9 0.00 0.00 14.75 0 0.9 0.00 0.00 10.00 0 With ﬁve linguistic terms 0.2 0.95 10.46 10.99 22 0.2 0.53 10.22 19.27 22 0.2 0.94 9.43 10.00 21 0.5 0.77 9.92 12.92 15 0.5 0.38 7.95 20.63 12 0.5 0.46 4.57 10.00 7 0.7 0.61 7.69 12.57 10 0.7 0.20 3.96 19.54 5 0.7 0.24 2.36 10.00 3 0.9 0.10 0.92 10.00 1 0.9 0.06 0.90 15.01 1 0.9 0.00 0.00 10.00 0
- 96. Results obtained in the genetic process Proposed approach Hong el al.’s approach Uniform fuzzy partition Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I Sup Fit Fsup Suit #1I With three linguistic terms 0.2 0.99 11.68 11.85 20 0.2 0.68 10.83 15.83 19 0.2 0.92 9.24 10.00 16 0.5 0.94 11.68 12.39 17 0.5 0.53 10.28 19.45 15 0.5 0.76 7.55 10.00 10 0.7 0.66 6.98 10.63 9 0.7 0.37 6.55 17.94 8 0.7 0.57 5.71 10.00 7 0.9 0.28 2.80 10.00 3 0.9 0.00 0.00 14.75 0 0.9 0.00 0.00 10.00 0 With ﬁve linguistic terms 0.2 0.95 10.46 10.99 22 0.2 0.53 10.22 19.27 22 0.2 0.94 9.43 10.00 21 0.5 0.77 9.92 12.92 15 0.5 0.38 7.95 20.63 12 0.5 0.46 4.57 10.00 7 0.7 0.61 7.69 12.57 10 0.7 0.20 3.96 19.54 5 0.7 0.24 2.36 10.00 3 0.9 0.10 0.92 10.00 1 0.9 0.06 0.90 15.01 1 0.9 0.00 0.00 10.00 0
- 97. Results obtained in the genetic process Hong el al.’s approach with the 2-tuples Support Fitness Fsup Suit #1Itemset With three linguistic terms 0.2 0.97 10.90 11.18 20 0.5 0.89 11.36 12.64 18 0.7 0.59 6.20 10.33 7 0.9 0.26 2.79 10.52 3 With ﬁve linguistic terms 0.2 0.93 10.18 10.93 22 0.5 0.64 7.39 11.80 11 0.7 0.41 0.476 11.60 6 0.9 0.08 0.91 10.92 1
- 98. Fitness vs Function Evaluation 1 Average Fitness Values. 0.8 0.6 0.4 0.2 0 0 2000 4000 6000 8000 10000 Evaluations The Proposed Approach Hong et al.'s Approach
- 99. Frequent 1-itemsets vs minsup Number of Large 1-itemsets 20 15 10 5 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Minimum Support The Proposed Approach Hong et al.'s Approach Uniform Fuzzy Partition
- 100. MFs w/o lateral displacement l1' = (l1,0.4) l2' = (l2,0.4) l3' = (l3,0.5) l1' = (l1,0.0) l2' = (l2,-0.2) l3' = (l3,0.0) l1' = (l1,-0.1) l2' = (l2,-0.2) l3' = (l3,0.2) X1 X2 X3 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' = (l1,0.0) l2' = (l2,0.0) l3' = (l3,0.4) l1' = (l1,0.1) l2' = (l2,-0.2) l3' = (l3,0.1) l1' = (l1,0.1) l2' = (l2,-0.5) l3' = (l3,0.1) X4 X5 X6 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' = (l1,-0.1) l2' = (l2,-0.1) l3' = (l3,0.4) l1' = (l1,0.0) l2' = (l2,-0.2) l3' = (l3,-0.2) l1' = (l1,0.0) l2' = (l2,-0.3) l3' = (l3,0.1) X7 X8 X9 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' = (l1,0.0) l2' = (l2,-0.2) l3' = (l3,0.2) X10 l1 l2 l3
- 101. Hong’s MFs l1' l2' l3' l1' l2' l3' l1' l2' l3' X1 X2 X3 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' l2' l3' l1' l2' l3' l1' l2' l3' X4 X5 X6 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' l2' l3' l1' l2' l3' l1' l2' l3' X7 X8 X9 l1 l2 l3 l1 l2 l3 l1 l2 l3 l1' l2' l3' X10 l1 l2 l3
- 102. #rules vs minsup minconf = 0.8 160000 140000 120000 Number of Rules 100000 80000 60000 40000 20000 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Minimum Support Proposed Approach Hong et al.'s Approach Uniform Fuzzy Partition
- 103. #rules vs minconf minsup = 0.2 90000 80000 70000 Number of Rules 60000 50000 40000 30000 20000 10000 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Minimum Confidence Proposed Approach Hong et al.'s Approach Uniform Fuzzy Partition
- 104. #rules vs minsup vs minsup 200000 Number of Rules 150000 100000 50000 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Minimum Support Conf = 0.5 Conf = 0.6 Conf = 0.7 Conf = 0.8 Conf = 0.9
- 105. #rules vs minsup vs minsup 200000 Number of Rules 150000 100000 50000 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Minimum Confidence Minsup = 0.1 Minsup = 0.2 Minsup = 0.3 Minsup = 0.4 Minsup = 0.5 Minsup = 0.6
- 106. Time vs #Transaction 30.00 25.00 Runtime (minutes) 20.00 15.00 10.00 5.00 0.00 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Number of Transactions Proposed Approach Hong et al.'s Approach
- 107. Time vs #Attribute 30.00 25.00 Runtime (minutes) 20.00 15.00 10.00 5.00 0.00 2 3 4 5 6 7 8 9 10 Number of Attributes Proposed Approach Hong et al.'s Approach
- 108. Time vs #Linguistic terms 70.00 Runtime (minutes) 60.00 50.00 40.00 30.00 20.00 3 4 5 6 7 Number of Linguistic Terms Proposed Approach Hong et al.'s Approach
- 109. Example of Rules If number if children is Low and Classic Fuzzy hours head worked last week is Low Association Rule then head’s personal income is Low (Factor of conﬁdence 0.87) If number if children is (Low, -0.16) and Rule with 2-Tuples hours head worked last week is (Low, -0.06) Representation then head’s personal income is (Low, 0.1) (Factor of conﬁdence 0.99)
- 110. Author’s conclusion
- 111. Author’s conclusion 2-tuples linguistic representation works!!
- 112. Discussions
- 113. T. Hong, C. Chen,Y. Wu,Y. Lee, Using divide-and-conquer GA strategy in fuzzy data mining, IEEE Symp. on Fuzzy Systems, Budapest, Hungary, 2004, pp. 116–121.
- 114. Pitfalls • domain knowledge & Symmetric assumption • ﬂowchart • Hong’s method • inadequate ﬁtness function • gray code and crossover • fuzzy association? • dataset • replication? • scalability
- 115. Pitfalls • domain knowledge & Symmetric assumption • ﬂowchart • Hong’s method n suitability(Cq ) = [overlap f actor(Cqk ) + coverage f actor(Cqk )] k=1 • inadequate ﬁtness function • gray code and crossover • fuzzy association? • dataset • replication? • scalability
- 116. Pitfalls • domain knowledge & Symmetric assumption • ﬂowchart • Hong’s method • inadequate ﬁtness function • gray code and crossover • fuzzy association? • dataset • replication? • scalability
- 117. Reference • L. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, in: G. Rawlin (Ed.), Foundations of Genetic Algorithms, Vol. 1, Morgan Kaufmann, Los Altos, CA, 1991, pp. 265–283. • F. Herrera, L. Martínez, A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Trans. Fuzzy Systems 8 (6) (2000) 746–752. • F. Herrera, M. Lozano, A.M. Sánchez, A taxonomy for the crossover operator for real- coded genetic algorithms: An experimental study. Int. J. Intell. Syst. 18 (2003) 309-338. • T. Hong, C. Chen, Y. Wu,Y. Lee, Using divide-and-conquer GA strategy in fuzzy data mining, in: IEEE Symp. on Fuzzy Systems, Budapest, Hungary, 2004, pp. 116–121. • T. Hong, C. Chen, Y. Wu,Y. Lee, quot;Genetic-Fuzzy Data Mining with Divide-and-Conquer Strategyquot;, IEEE Transactions on Evolutionary Computation 12 (2) 252-265. • T. Hong, C. Kuo, S. Chi, Trade-off between time complexity and number of rules for fuzzy mining from quantitative data, Journal of Uncertain Fuzziness Knowledge-Based Systems 9 (5) (2001) 587–604. • H. Ishibuchi, T. Nakashima, T.Yamamoto, Fuzzy association rules for handling continuous attributes, in: IEEE Internat. Symp. on Industrial Electronics Proceedings, Pusan, Korea, 2001, pp. 118–121. • P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining, Addison Wesley, May 2005.
- 118. Thank you!
- 119. Questions?

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment