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Data mining basic fundamentals
The document discusses data mining fundamentals, defining data, information, and knowledge, and their interrelationships. It explains data mining as the extraction of hidden predictive information from large databases, emphasizing its practical applications in business and technology. Additionally, it covers machine learning, data warehousing, and various data mining techniques such as classification, clustering, and regression analysis.
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Data mining basic fundamentals
- 1. Data Mining Fundamentals Presentedby S.P. Siddique Ibrahim AP/CSE Kumaraguru College of Technology 09/06/18 1
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- 3. DATA • Fact, Numbers(or) text. • Different format and different databases. • Examples: • College department,CoE,Hostal, Payroll, • Police traffic, fraud data • RTO • Revenue 09/06/18 3
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- 5. DATA Contd., • Operationalor Transactional – sales, cost, inventory, payroll • Non operational- industry sales , forecasting data, micro economic data • Meta data – logical database design (or) dictionary design 09/06/18 5
- 6. Information • The pattern,relationships among all this data can provide information. • Example: • Analysis retail point of sale transaction • Product –selling and date 09/06/18 6
- 7. Knowledge • Information canbe converted into knowledge about historical patterns and future trends. • Example: • Summary information of petrol/Diesel usage. 09/06/18 7
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- 11. General Definition ofMining • Mining is the process of extracting commercially valuable minerals resources from earth’s surface. • -Stones • -Ores • -Solid Fuels • -Crude Oil Surface Mining Subsurface mining 09/06/18 11
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- 14. Definition contd., • Analyzingdata from different perspective and summarize information. • Information-revenue • Technically, it is the process of finding correlation (or) patterns among dozens of fields in large relational databases. • Example: Purchase pattern, credit card 09/06/18 14
- 15. Data Mining • Datamining, “the extraction of hidden predictive information from large databases”. • It is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. • The main focus of data mining process is to obtain information from the data and converted it into an knowledgeable and reasonable structure for further use. 09/04/18 15 09/06/18 15
- 16. Different Mining inReal Time 09/06/18 16
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- 19. What is MachineLearning?? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. 09/06/18 19
- 20. Why data miningfor you? • Research Opportunities. • High Opening. • Handling Big data. • Analysis capability • Future 09/06/18 20
- 21. What can datamining do? • Company competition • Internal and external factor • Analyze real feelings of customer/Auditions 09/06/18 21
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- 24. What is pattern?How it would be? • Customer buying pattern • Mobile pattern • Bus ticket booking • Cricket analysis • Political parties election freebies • Credit card 09/06/18 24
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- 32. What is DataWarehouse???? A data warehousing is a technique for collecting and managing data from varied sources to provide meaningful business insights. Online Airline/Bus/Railway ticket booking 09/06/18 32
- 33. Data warehouse • Centralizedstorage • Free use of data 09/06/18 33
- 34. How does datamining work? • Classes – stored data is used to locate data in predetermined groups. • Example: • Restaurant chain 09/06/18 34
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- 36. What is this?Classify it!!!! 09/06/18 36
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- 38. Can u ableto Identify Now!!?? 09/06/18 38
- 39. Cluster • Data itemsare grouped based on similarity(logical relationship) (or) consumer preferences. • • Example: • Data used for market segments 09/06/18 39
- 40. Association • Mined forassociations. Example: Market basket analysis 09/06/18 40
- 41. Sequential patterns • Datais mined to anticipate behavior patterns and trends. 09/06/18 41
- 42. Regression Analysis • Ameasure of the relation between the mean value of one variable(E.g. Output) and corresponding values of other variables(E.g Time and cost) 09/06/18 42
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