1. 1 Context-aware Data Mining using Ontologies Sachin Singh, Pravin Vajirkar, and Yugyung Lee Springer-Verlag Berlin Heidelberg 2003, pp405-418 Reporter: Hsin-Chan Tsai

2. 2 Outline Introduction What is Context-Awareness A Motivation Example Context-aware data mining framework Ontology Design Experiment Results Conclusion

3. 3 Data mining and context Extracting interesting information from large collections of data. The applications of data mining Require a dynamic and resilient model that is aware of a wide variety of diverse and unpredictable context. Context consist of circumstantial aspects of the user and domain.

4. 4 Context will … Be represented on ontology Be automatically captured during data mining process Allow the adaptive behavior to carry over to powerful data mining

5. 5 Definition of context types Domain Context Location Context Data Context User Context

6. 6 Domain Context A kind of patient-centric data Family members Medical history

7. 7 Location Context Living area is relative to health issue When people living in coastal region → have less probability of getting goiter Living in the country side →fewer to get high blood pressure

8. 8 Data Context Some dataset to pickup Heart attack Body temperature

9. 9 User Context User Identity Context Expertise field Authorization of tasks User History Context User queries for a particular information

10. 10 A Motivation Example of Context-aware Data mining Location Not a input the data mining process but a context factor Family-History A specific case of Domain context Smoke-Disease From when Cigs per day When quit (period)

11. 11 1. (age) Age in years 2. (sex)Sex – Value 1: Male and Value 0: Female) 3. (chest pain) chest pain type – Value 1: Typical angina, Value 2: Atypical angina – Value 3: Non-anginal pain, Value 4: Asymptomatic 4. (trestbps) resting blood pressure (in mm Hg on admission to the hospital) 5. (chol) Serum cholestoral in mg/dl 6. (fbs) (Fasting blood sugar >120 mg/dl) – Value 1: True and Value 0: False 7. (restecg)resting electrocardiographic results – Value 0: Normal – Value 1: Having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of >0.05 mV) – Value 2: Showing probable or definite left ventricular hypertrophy by Estes’ criteria 8. (thalach) Maximum heart rate achieved 9. (exang) Exercise induced angina (1 = yes; 0 = no) 10.(oldpeak) ST depression induced by exercise relative to rest 11.(slope) The slope of the peak exercise ST segment – Value 1: upsloping, Value 2: flat, Value 3: downsloping 12.(ca) Number of major vessels (0-3) colored by flourosopy 13.(thal) the heart status – Value 3: Normal, Value 6: Fixed defect, Value 7: Reversable defect 14.(Family-Hist)- History of any heart disease within immediate family – Value 1: True, Value 0: False 15.(Smoke-Disease) - Symptoms of smoke disease 16.(Location) - Location of the person where he lives. 17.(num)Diagnosis of heart disease (angiographic disease status) – Value 0: <50% Diameter narrowing – Value 1: >50% Diameter narrowing (in any major vessel: attributes 59 through 68 are vessels) The attribute of the dataset in details

12. 12 Context-Aware Data mining Model Phase1 Pick Join Trim Phase2:different types of mining processes Cascading mining process Sequential mining process Iterative mining process Parallel fork Aggregating Mining process Three different schemes Context factors C i A set of tuples T i A set of attributes A i

13. 13 Architecture of Context-Aware Data Mining Framework User Profile Domain Ontology Data Ontology Process Ontology Location Context User Context Domain Context Data Context Query Analyzer UI Component (Web server) Service Ontology Query Processor Data Mining Tool Data Processing Datasets Context Factor Detection plane Architecture Diagram

14. 14 Ontology Design Main concept Service Process

15. 15

16. 16 Experiment results Case 1: Classification Tree exang= no oldpeak _ 1: < 50 (190.0/27.0) oldpeak > 1 slope = down: > 50 1 (0.0) slope= flat sex = female: < 50 (3.0/1.0) sex = male: > 50 1 (8.0) slope = up: < 50 (3.7) exang = yes: > 50 1 (89.3/19.3)

17. 17 Experiment results Case 2: Classification Tree exang= no oldpeak _ 1: < 50 (129.0/24.0) oldpeak > 1 slope = down: > 50 1 (0.0) slope = flat: > 50 1 (8.0) slope = up: < 50 (2.0) exang= yes chest pain = typ angina: > 50 1 (0.0) chest pain = asympt: > 50 1 (62.0/6.0) chest pain = non anginal age _ 55: > 50 1 (3.0/1.0) age > 55: < 50 (2.0) chest pain = atyp angina oldpeak _ 1.5: < 50 (4.0/1.0) oldpeak > 1.5: > 50 1 (3.0)

18. 18 Experiment results Case 3: Classification Tree exang = no: < 50 (64.82/4.0) exang = yes thalach _ 108: > 50 1 (3.04/0.04) thalach> 108 chol _ 254: < 50 (4.0) chol > 254 thalach _ 127: < 50 (4.08/1.0) thalach > 127: > 50 1 (3.06/0.06)

19. 19 Experiment results context factor Sex : male / female Age : in years Pain location : substernal / otherwise Output 0: <50% diameter narrow 1:>50% diameter narrow The result confirm the effectiveness of user of context factor in data mining.

20. 20 Conclusion Different types of context and ontology Domain 、 Location 、 Data and User context Domain 、 Data 、 Process and Service ontology