1. AOI-ags Algorithms and inside Stories the School of Computing and Engineering of the University of Huddersfield Lizhen Wang July 2008

3. Outline  Introduction  Attribute-Oriented Induction Based on Attributes’ Generalization Sequences (AOI-ags)  An Optimization AOI-ags Algorithm  Interestingness of AGS  Performance Evaluation and Applications Chapter 5

4. Introduction Chapter 5 (1). Attribute threshold control (2). Relation threshold control

5. Outline  Introduction  Attribute-Oriented Induction Based on Attributes’ Generalization Sequences (AOI-ags)  An Optimization AOI-ags Algorithm  Interestingness of AGS  Performance Evaluation and Applications Chapter 5

6. AOI-ags Method (1)  an attribute is generalized earlier or latter will not affect the final generalization result.  a generalization result is the same no matter that it is obtained by generalizing gradually or directly up to the k-th level

7. Chapter 5 AOI-ags Method (2)

8. Chapter 5 AOI-ags Method (3)

9. Chapter 5 AOI-ags Method (4)

10. Outline  Introduction  Attribute-Oriented Induction Based on Attributes’ Generalization Sequences (AOI-ags)  An Optimization AOI-ags Algorithm  Interestingness of AGS  Performance Evaluation and Applications Chapter 5

11. An Optimization AOI-ags Algorithm (1) (1). AOI-ags and Partition : an equivalence partition of r under X intersection partition

12. Chapter 5 An Optimization AOI-ags Algorithm (2)

13. Chapter 5 An Optimization AOI-ags Algorithm (3)

14. Chapter 5 An Optimization AOI-ags Algorithm (4) (2) Searching Space and Pruning Strategies

15. Chapter 5 An Optimization AOI-ags Algorithm (5) Example 5.2 Given two attributes A 1 and A 2, the Heights of the concept hierarchy trees are l 1 =2, l 2 =3, then the searching space is showed as figure 5.2

16. Chapter 5 An Optimization AOI-ags Algorithm (6)

17. Chapter 5 An Optimization AOI-ags Algorithm (7)

18. Chapter 5 (3) Equivalence Partition Trees and Calculating (1) Definition 5.7 The equivalence partition tree of the attribute A

19. Chapter 5 Algorithm 5.2: An optimization algorithm of AOI-ags

20. Chapter 5 Algorithm 5.2: An optimization algorithm of AOI-ags

21. Outline  Introduction  Attribute-Oriented Induction Based on Attributes’ Generalization Sequences (AOI-ags)  An Optimization AOI-ags Algorithm  Interestingness of AGS  Performance Evaluation and Applications Chapter 5

22. Interestingness of AGS (1)

23. Chapter 5 Interestingness of AGS (2)

24. Chapter 5 Interestingness of AGS (3)

25. Outline  Introduction  Attribute-Oriented Induction Based on Attributes’ Generalization Sequences (AOI-ags)  An Optimization AOI-ags Algorithm  Interestingness of AGS  Performance Evaluation and Applications Chapter 5

26. Performance Evaluation and Applications (1) Figure 5.4 Performance of algorithms using synthetic datasets

27. Chapter 5 Performance Evaluation and Applications (2) Figure 5.5 Characters of fast re-generalization for the two algorithms

28. Chapter 5 Applications in a Real Dataset The followings are some examples: --“Tricholoma matsutake” ⇒ 40% grows in the forest and meadow whose elevation is from 3300 to 4100 meter of Lijiang. --“Angiospermae” ⇒ 80% grows in the forest 、 scrub and meadow whose elevation is from 2400 to 3900 meter of Lijiang and Weixi. --Lijiang ⇒ There are a plenty of plants species in severe danger such as “Tricholoma matsutake”, “Angiospermae”, “Gymnospermae”.

29. Conclusions  In this chapter, first, by introducing a new concept of attributes’ generalization sequences, AOI-ags method was proposed.  Second, an optimization AOI-ags algorithm was discussed.  Third, by defining the interestingness of AGS, the selection problem of AGS is solved.  Fourth, Performance Evaluation and Applications Chapter 5

30. Thanks! Any questions?