Ubiquitous Personalized Information

Transcription

1 Ubiquitous Personalized Information Processing & Services Xindong Wu Department of Computer Science University of Vermont, USA Infrasec Workshop, January 9,

2 Ubiquitous Personalized Information Processing & Services: Objectives P1: Demand - Driven Information Integration P4: Security and Privacy P3: User Interest Modeling P2: Mining and Analysis A positive cycle with P1: Demand-driven integration of information sources P2: Mining and analysis P3: User interest modeling P4: Security and privacy. Infrasec Workshop, 1/9/2010 2

3 Xindong Wu Technical Interests: Deduction Induction 1988 Expert Systems 1990 Expert Systems 1995 数据挖掘 2004 数据挖掘 3

4 Xindong Wu Two Professional Babies 4

5 Xindong Wu TKDE and KDD-07 TKDE Editor-in-Chief, 1/1/ /31/200812/31/2008 5

6 Data Mining: Algorithms & Applications 6

7 1. Classification #1. C4.5: Quinlan, J. R C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, #3. K Nearest Neighbours (knn): Hastie, T. and Tibshirani, i R Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), #4. Naive Bayes: Hand, D.J., Yu, K., Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, Data Mining: Algorithms & Applications 7

8 2. Statistical Learning #5. SVM: Vapnik, V. N The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc. #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Data Mining: Algorithms & Applications 8

9 3. Association Analysis #7. Apriori: Rakesh Agrawal al and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94. #8. FP-Tree: Han, J., Pei, J., and Yin, Y Mining frequent patterns without candidate generation. In SIGMOD '00. Data Mining: Algorithms & Applications 9

10 4. Link Mining #9. PageRank: Brin, S. and Page, L The anatomy of a large-scale hypertextual Web search engine. In WWW-7, #10. HITS: Kleinberg, J. M Authoritative sources in a hyperlinked environment. In Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, Data Mining: Algorithms & Applications 10

11 5Cl 5. Clustering #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate i t observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96. Data Mining: Algorithms & Applications 11

12 6E 6. Ensemble Learning #13. AdaBoost: Freund, Y. and Schapire, R. E A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), Data Mining: Algorithms & Applications 12

13 7. Sequential Patterns #14. GSP: Srikant, R. and Agrawal, R Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01. Data Mining: Algorithms & Applications 13

14 8It 8. Integrated tdmii Mining #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98. Data Mining: Algorithms & Applications 14

15 9R 9. Rough hst Sets #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, Data Mining: Algorithms & Applications 15

16 10. Graph Mining i #18. gspan: Yan, X. and Han, J gspan: Graph-Based Substructure Pattern Mining. In ICDM '02. Data Mining: Algorithms & Applications 16

17 The Top 10 Algorithms #1: C4.5 #2: K-Means #3: SVM #4: Apriori #5: EM #6: PageRank #7: AdaBoost #7: knn #7: Naive Bayes #10: CART Top 10 Algorithms in Data Mining: Xindong Wu and Vipin Kumar 17

18 Application 1. Diagnosis i There are 2 alternative hypotheses: 1. a particular form of cancer (+) 2. the cancer does not exist (-) Prior knowledge over the entire population p of people: p only have this disease Lab test is only an imperfect indicator: 1. A correct positive result in only 98% of the cases when the cancer exists; 2. A correct negative result with 97% reliability To summarize, 1. P(cancer) = P(~cancer) = P(+ cancer) = 0.98 P(- cancer) = P(+ ~cancer) = P(- ~cancer) = 0.97 Suppose a patient has a positive lab test result. Should we diagnose him/her as having the cancer? The answer is no! Data Mining: Algorithms & Applications 18

19 Application 2: OIDM Data Mining: Algorithms & Applications 19

20 OIDM (2) Data Mining Tools Classification Tools Association Analysis Tools Clustering Tools Tree Construction Tools Rule Generating Tools Apriori CobWeb K-Means C4.5 C4.5Rules, OneR, Prism, HCV Data Mining: Algorithms & Applications 20

21 Application i 3: User Modeling Data Mining: Algorithms & Applications 21

22 Application i 4: Noise Handling Data Mining: Algorithms & Applications 22

23 The Russell Paradox Nobel laureate in Literature 1950 Also a philosopher, logician and mathematician There was once a barber, Wherever he lived, all of the men in his town either shaved themselves or were shaved by the barber. And the barber only shaved the men who did not shave themselves. Did the barber shave himself? Can we solve the Russell paradox? Yes, Mathematically, type theory (by Russell) and axiomatic set theory In data mining, i we treat t it as systematic ti noise! January 9,

24 More to Logic Can we solve the Russell Paradox (in data mining)? i If so, how? Change the question data before answering It was said (by Russell?) everything follows logic except Love Religion Wars What is the next step for noise-tolerant data mining? Domain knowledge Noise profiling Unknown noise types. January 9,

25 Phase 1. Web News Recognition and Filtering Training Pages Trai ning Feature Represention Feature Vectors Learning The Web page isn t Web news. URL Rec ogn itio n Web Page Fetching & Feature Representation Feature Vector Web News Identifier Finished Application 5: Web News Filtering and Filt erin g Web page of the URL The Web page is Web news. Extraction Rules Web Information Extractor Summarization Phase 2. Web News Summarization Su mm ari zati on Word Segmentation Named Entity Identification HowNet Keyphrases and their lexical chains News Title and Content URL Compute the TFIDF Value Extract Candidate Phrases Compute Word Similarity and Co-occurrence Frequency Construct Lexical Chains

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28 Application 6: Information Fusion with Meta Search Hyperlink: query = Xindong Wu Security

29 Challenges with Information Fusion Intelligent Informatics: Connect seemingly irrelevant information items Whether X is Y s wife? Did the first ladies meet before? Active information fusion Something happens, why? Network analysis Sub-network identification Information diffusion Confidence of a node and confidence of fusion Adaptive interest modeling and monitoring Sequential pattern mining Event/anomaly detection. ti

30 Conclusions Ubiquitous personalized information processing involves information aggregation, analysis/mining, user interest modeling, and security/privacy Dt Data mining ii and sequence matching thi (with Web information) are 2 research frontiers for ubiquitous personalized information processing. Infrasec Workshop, 1/9/