Содержание
- 2. Artificial intelligent An area of study in the field of computer science. Artificial intelligence is concerned
- 3. Machine learning The field of machine learning is concerned with the question of how to construct
- 4. Data mining Data mining is the extraction of implicit, previously unknown, and potentially useful information from
- 5. Text mining Text mining is a variation on a field called data mining,that tries to find
- 6. Process model for Data/Text mining Cross Industry Standard Process for Data Mining
- 7. Data mining Application: Financial data analysis (loan payment prediction, consumer credit policy analisys, price movement, detection
- 8. Data mining Type of attributes: Nominal (categorical) Binary Ordinal Numeric
- 9. Data mining Data preparation: Representative samples Categorial value Normalization Missing and empty value Anomaly detection Smooth
- 10. Data mining Tasks: Classification Regression Clustering Associating rule learning
- 11. Data mining Type of learning: Hold-out=Training set (70%) + Validation set (30%) Cross-validation
- 12. Data mining
- 13. Data mining Example: “Heart desease prediction” I = {id1, id2....} //patient Ij = {gender, age, smoking,
- 14. Data mining
- 15. Data mining Example: Electricity market price forecast I = {id1, id2....} //time Ij = {Date, time,
- 16. Data mining
- 17. Data mining
- 18. Data mining Naive Bayes
- 19. Data mining Support Vector Machine (SVM)
- 20. Data mining Decision tree B. Dawson, R.G. Trapp “Basic & Clinical Biostatistics, 4e”
- 21. Data mining Neural network: formal neuron F
- 22. Data mining Neural network
- 23. Data mining
- 24. Data mining
- 25. Data mining Example: Clustering e-Banking Customer I = {id1, id2....} //transaction Ij ={date, time, status_of_transaction, type_of_transaction,
- 26. Data mining K-means
- 27. Data mining EM-algorithm
- 28. Data mining Agglomerative algorithm Divisive algorithm
- 29. Data mining: tasks
- 30. Data mining: tasks Genetic algorithms:
- 31. Text Mining Information retrieval (IR) + natural language processing (NLP)
- 32. Text mining Text preparation: Tokenization Removal stop-words Stemming Lemmatization Bag-of-Words (TF-IDF)
- 33. Text mining Tasks: Classification Clustering Building ontology Information extraction Sentiment analysis Document summarisation
- 34. Text Mining Text classification:
- 35. Text Mining Clustering:
- 36. Text Mining Ontology: http://ontologies.sti-innsbruck.at/acco/ns.html
- 37. Text Mining Information extraction:
- 38. Text Mining Sentiment analysis:
- 39. Text Mining Document summarization:
- 40. Text Mining Not covered in this lecture: Mathematical apparatus Time series Feature selection Fuzzy logic Genetic
- 41. References Books: Чубукова И. А. Data Mining: учебное пособие. Барсегян А. А., Куприянов М.С., Степаненко В.В.,
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