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Outline Introduction Mining by Learning Conclusion




         A Unified Approach to Mining Complex
      Time-Series Data...
Outline Introduction Mining by Learning Conclusion




  1   Introduction
         Aspects of Sequential Data Mining
     ...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion   Aspects of the Problem A Unified Approach


Aspects of Sequential Data...
Outline Introduction Mining by Learning Conclusion     Aspects of the Problem A Unified Approach


Various Approaches or A ...
Outline Introduction Mining by Learning Conclusion     Aspects of the Problem A Unified Approach


Various Approaches or A ...
Outline Introduction Mining by Learning Conclusion     Aspects of the Problem A Unified Approach


Various Approaches or A ...
Outline Introduction Mining by Learning Conclusion     Aspects of the Problem A Unified Approach


Various Approaches or A ...
Outline Introduction Mining by Learning Conclusion     Aspects of the Problem A Unified Approach


Various Approaches or A ...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning the Temporal Str...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Hidden Markov Model (HMM)...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Hidden Markov Model (HMM)...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Hidden Markov Model (HMM)...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Hidden Markov Model (HMM)...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Fixed nth-order Hidden Ma...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Fixed nth-order Hidden Ma...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Fixed nth-order Hidden Ma...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Fixed nth-order Hidden Ma...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Variable-length Hidden Ma...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Variable-length Hidden Ma...
Outline Introduction Mining by Learning Conclusion                          Hidden Markovian Model Various HMMs VLHMM


Va...
Outline Introduction Mining by Learning Conclusion                          Hidden Markovian Model Various HMMs VLHMM


Va...
Outline Introduction Mining by Learning Conclusion   Hidden Markovian Model Various HMMs VLHMM


Learning Variable-length ...
Outline Introduction Mining by Learning Conclusion   Mining Patterns Contributions


Mining Various Kinds of Patterns
    ...
Outline Introduction Mining by Learning Conclusion   Mining Patterns Contributions


Our Contribution

    A unified framew...
Outline Introduction Mining by Learning Conclusion   Mining Patterns Contributions




                   Thank You for Yo...
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  1. 1. Outline Introduction Mining by Learning Conclusion A Unified Approach to Mining Complex Time-Series Data for Various Kinds of Patterns Yi Wang1 J.H. Feng1 J.Y. Wang1 Z.Q. Liu2 1 Department of Computer Science, Tsinghua University, Beijing, 100084, China 2 School of Creative Media, City University of Hong Kong, Hong Kong IEEE ICDM Conference, 2007 Wang, et al Mining Complex Time-Series Data
  2. 2. Outline Introduction Mining by Learning Conclusion 1 Introduction Aspects of Sequential Data Mining Various Approaches or A Unified One 2 Mining by Learning Learning the Temporal Structure as A Graph Various Kinds of Hidden Markovian Models Learning VLHMM 3 Conclusion Mining Various Kinds of Patterns Contributions Wang, et al Mining Complex Time-Series Data
  3. 3. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, Various Mining Goals: Difficulties: Wang, et al Mining Complex Time-Series Data
  4. 4. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: Difficulties: Wang, et al Mining Complex Time-Series Data
  5. 5. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: periodic pattern, Difficulties: two periodic patterns: one with 3 realizations, the other with 2. Wang, et al Mining Complex Time-Series Data
  6. 6. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: periodic pattern, search-by-example, Difficulties: Wang, et al Mining Complex Time-Series Data
  7. 7. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: periodic pattern, search-by-example, frequent atomic pattern, Difficulties: Wang, et al Mining Complex Time-Series Data
  8. 8. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: periodic pattern, search-by-example, frequent atomic pattern, Difficulties: uncertainty on the y-axis (e.g., noise), match? Wang, et al Mining Complex Time-Series Data
  9. 9. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Aspects of Sequential Data Mining Various Sequence Types: univariate/multivariate, integer (discrete)/ real (continous), Various Mining Goals: periodic pattern, search-by-example, frequent atomic pattern, Difficulties: uncertainty on the y-axis matches with which? or (e.g., noise), matches with both? uncertainty on the x-axis (e.g., time scale). Wang, et al Mining Complex Time-Series Data
  10. 10. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Various Approaches or A Unified One Previous Research: Various approaches Our Work: Various types of Various mining Various mining sequences and algorithms resultsd difficulties The Unified Approach: Wang, et al Mining Complex Time-Series Data
  11. 11. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Various Approaches or A Unified One Previous Research: Various approaches Our Work: Various types of Various mining Various mining sequences and algorithms resultsd A unified approach difficulties The Unified Approach: Wang, et al Mining Complex Time-Series Data
  12. 12. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Various Approaches or A Unified One Previous Research: Various approaches Our Work: Various types of Various mining Various mining sequences and algorithms resultsd A unified approach difficulties The Unified Approach: Learning Temporal Graph Learns various types of hidden structure algorithms sequences by hidden Markovian as directed for Markovian models; model graph mining Wang, et al Mining Complex Time-Series Data
  13. 13. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Various Approaches or A Unified One Previous Research: Various approaches Our Work: Various types of Various mining Various mining sequences and algorithms resultsd A unified approach difficulties The Unified Approach: Learning Temporal Graph Learns various types of hidden structure algorithms sequences by hidden Markovian as directed for Markovian models; model graph mining represents the temproal structure by a graph; and Wang, et al Mining Complex Time-Series Data
  14. 14. Outline Introduction Mining by Learning Conclusion Aspects of the Problem A Unified Approach Various Approaches or A Unified One Previous Research: Various approaches Our Work: Various types of Various mining Various mining sequences and algorithms resultsd A unified approach difficulties The Unified Approach: Learning Temporal Graph Learns various types of hidden structure algorithms sequences by hidden Markovian as directed for Markovian models; model graph mining represents the temproal structure by a graph; and mines various patterns by well-studies graph algorithms. Wang, et al Mining Complex Time-Series Data
  15. 15. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  16. 16. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  17. 17. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  18. 18. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  19. 19. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  20. 20. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  21. 21. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  22. 22. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning the Temporal Structure as A Graph Wang, et al Mining Complex Time-Series Data
  23. 23. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Hidden Markov Model (HMM) Given number of states, S, the number of contexts is S. Short contexts → inaccurate modeling. Wang, et al Mining Complex Time-Series Data
  24. 24. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Hidden Markov Model (HMM) Given number of states, S, the number of contexts is S. Short contexts → inaccurate modeling. Wang, et al Mining Complex Time-Series Data
  25. 25. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Hidden Markov Model (HMM) Given number of states, S, the number of contexts is S. Short contexts → inaccurate modeling. Wang, et al Mining Complex Time-Series Data
  26. 26. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Hidden Markov Model (HMM) Given number of states, S, the number of contexts is S. Short contexts → inaccurate modeling. Wang, et al Mining Complex Time-Series Data
  27. 27. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Fixed nth-order Hidden Markov Model (n-HMM) Given number of states, S, and the length of context, n, the number of contexts is S n . Long contexts → accurate modeling, but inefficient learning. Wang, et al Mining Complex Time-Series Data
  28. 28. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Fixed nth-order Hidden Markov Model (n-HMM) Given number of states, S, and the length of context, n, the number of contexts is S n . Long contexts → accurate modeling, but inefficient learning. Wang, et al Mining Complex Time-Series Data
  29. 29. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Fixed nth-order Hidden Markov Model (n-HMM) Given number of states, S, and the length of context, n, the number of contexts is S n . Long contexts → accurate modeling, but inefficient learning. Wang, et al Mining Complex Time-Series Data
  30. 30. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Fixed nth-order Hidden Markov Model (n-HMM) Given number of states, S, and the length of context, n, the number of contexts is S n . Long contexts → accurate modeling, but inefficient learning. Wang, et al Mining Complex Time-Series Data
  31. 31. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Variable-length Hidden Markov Model (VLHMM) Not all contexts have to be extended to fixed length of n; Contexts have variable lengths: the shortest, but long enough to accurately determine the next state; Learning the minimum set of contexts for accurate modeling. Wang, et al Mining Complex Time-Series Data
  32. 32. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Variable-length Hidden Markov Model (VLHMM) Not all contexts have to be extended to fixed length of n; Contexts have variable lengths: the shortest, but long enough to accurately determine the next state; Learning the minimum set of contexts for accurate modeling. 1 2 3 HMM Wang, et al Mining Complex Time-Series Data
  33. 33. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Variable-length Hidden Markov Model (VLHMM) Not all contexts have to be extended to fixed length of n; Contexts have variable lengths: the shortest, but long enough to accurately determine the next state; Learning the minimum set of contexts for accurate modeling. 1 2 1 1 1 2 1 3 2 1 3 3 2 2 3 2 3 2 3 3 1 HMM n-HMM Wang, et al Mining Complex Time-Series Data
  34. 34. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Variable-length Hidden Markov Model (VLHMM) Not all contexts have to be extended to fixed length of n; Contexts have variable lengths: the shortest, but long enough to accurately determine the next state; Learning the minimum set of contexts for accurate modeling. 1 2 1 2 2 1 2 1 1 1 1 2 1 3 2 1 3 3 3 2 1 3 2 2 2 3 2 3 3 3 2 3 3 1 3 3 3 3 HMM n-HMM VLHMM Wang, et al Mining Complex Time-Series Data
  35. 35. Outline Introduction Mining by Learning Conclusion Hidden Markovian Model Various HMMs VLHMM Learning Variable-length Hidden Markov Model (VLHMM) The number of contexts is unknown before learning, even with the number of states, S, given; This situation is called “unknown model structure” in learning theory, and is the most of the four types of learning problems; As the EM algorithm cannot learn the model structure, we derived a structural-EM algorithm to learn the model; Optimizing a Minimum-Entropy criterion to learn the minimum set of contexts, and optimizing the Maximum-likelihood criterion the estimate the model parameters. Wang, et al Mining Complex Time-Series Data
  36. 36. Outline Introduction Mining by Learning Conclusion Mining Patterns Contributions Mining Various Kinds of Patterns Align sequence with temporal structure The Viterbi algorithm can setup a map from each element in the sequence to a context in the graph. (Partial) Periodic Pattern Finding cyclic paths in the graph. Many algorithms are developed to do this. Search-by-Example Input the example to the Viterbi algorithm, outputs a path that is “most likely” with the example. Frequent Atomic Pattern Select those contexts that frequently appear in the training sequence. Wang, et al Mining Complex Time-Series Data
  37. 37. Outline Introduction Mining by Learning Conclusion Mining Patterns Contributions Our Contribution A unified framework – mining by learning Mining from the learned temporal structure using well-studied graph algorithms; “Hidden” model support learning various kinds of sequences; Probabilistic transitions (esp, self-transitions) encode uncertainty in time-scale; Output p.d.f.s encode noises. VLHMM for efficient and accurate learning and mining Optimizing two criteria simultaneously by developing a structural-EM algorithm; Minimum-Entropy criteria → minimum number of parameters, efficient and effective learning; Maximum-Likelihood criteria → accurate learning of the temporal structure. Wang, et al Mining Complex Time-Series Data
  38. 38. Outline Introduction Mining by Learning Conclusion Mining Patterns Contributions Thank You for Your Attention More details and demos can be accessed online at: http://dbgroup.cs.tsinghua.edu.cn/wangyi/vlhmm Wang, et al Mining Complex Time-Series Data
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