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1. Why Data Mining? - Correct Answer: Explosive data growth (in KB, MB, GB,TB, PB,
EB, and ZB)
2. What is data mining? - Correct Answer: Knowledge discovery from data (Extraction
of interesting patterns or knowledge from huge amounts of data.)
3. Benefits of data mining - Correct Answer: Scalability and efficiency
4. The four views of data mining - Correct Answer: Data, Application, Knowledge,
Technique
5. What are the 5Vs of Data Mining? - Correct Answer: Volume, Variety, Velocity,
Veracity, Value
6. Relational, transactional data (Data View) - Correct Answer: E.g., student records,
bank accounts, store purchases
7. Sequential, temporal, streaming data (Data View) - Correct Answer: E.g., gene
sequences, stock prices, sensor readings
,8. Spatial, spatial-temporal data (Data View) - Correct Answer: E.g., land use, bird
migration, traffic condition
9. Text, multimedia, Web data (Data View) - Correct Answer: E.g., news articles,
audio/video/image data, hypertext
10. Graph, network data (Data View) - Correct Answer: E.g., social network, power grid,
co-authorship
11. Market Analysis, target advertisement (Application View) - Correct Answer: E.g.,
customer profiling, product recommendation
12. Healthcare, medical research (Application View) - Correct Answer: E.g., disease
diagnosis, patient care, drug discovery
13. Science and engineering (Application View) - Correct Answer: E.g., air pollution,
marine life, electric vehicles
14. Security (Application View) - Correct Answer: E.g., surveillance, intrusion/crime,
fraud, cyberattack
15. Government, nonprofit (Application View) - Correct Answer: E.g., urban planning,
traffic control, education
16. Frequent pattern , correlation (Knowledge View) - Correct Answer: E.g., Songs
listened together or in certain sequence
,17. Categorization (Knowledge View) - Correct Answer: E.g., Similarity among user with
certain purchases, differences between two patient groups
18. Anomaly, outliers (Knowledge View) - Correct Answer: E.g., sensor errors, fraud
activities, extreme events
19. Changes over time (Knowledge View) - Correct Answer: E.g., emerging new patterns,
shift of user interest
20. What are the five different techniques for data mining? - Correct Answer: Frequent
pattern analysis, classification/prediction, clustering, anomaly detection, trend and
evolution analysis
21. Frequent Pattern Analysis - Correct Answer: Includes frequent itemset, frequent
sequence, frequent structure, association rules, correlation analysis
22. Classification - Correct Answer: Includes pre-defined classes, training data, and
distinguishable classes
23. Prediction - Correct Answer: Includes numerical prediction (continuous) values (e.g.
weather, stock price, traffic)
24. Clustering - Correct Answer: Includes no pre-defined classes, intra-cluster similarity,
inter-cluster dissimilarity
25. Anomaly Detection - Correct Answer: Includes anomalies or outliers (e.g. error,
noise, fraud, extreme events)
, 26. Trend and Evolution Analysis - Correct Answer: Includes changes over time, overall
trend, periodical patterns, anomalies (e.g. Google Trends)
27. What Steps Form The Data Mining Pipeline? - Correct Answer: Data Understanding,
Data Preprocessing, Data Warehousing, Data Modeling, Pattern Evaluation
28. Data Understanding - Correct Answer: Answering questions like: What types of data?
What do they look like?
29. Includes statistics and visualization
30. observes similarity vs dissimilarity
31. data preprocessing - Correct Answer: Preparing the data for the mining process,
includes the following operations: Data Integration, Data Transformation, Data
Reduction, Data Cleaning
32. What Potential Issues Are There With Data? - Correct Answer: Missing data, errors,
inconsistency
33. Data Warehousing - Correct Answer: the collection, storage, and retrieval of data in
electronic files. Includes operational data. Can involve a data cube and OLAP
34. Data Modeling - Correct Answer: Step that involves the five technique views of data
mining
35. Pattern Evaluation - Correct Answer: Involves finding interesting patterns from data,
use of evaluation metrics and model selection