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Introduction to KDD. Evolution of Database Technology  1950s: First computers, use of computers for census.  1960s: Data collection, database creation.

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Presentation on theme: "Introduction to KDD. Evolution of Database Technology  1950s: First computers, use of computers for census.  1960s: Data collection, database creation."— Presentation transcript:

1 Introduction to KDD

2 Evolution of Database Technology  1950s: First computers, use of computers for census.  1960s: Data collection, database creation (hierarchical and network models)  1970s: Relational data model, relational DBMS implementation  1980s: Ubiquitous RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc)  1990s: Data mining and data warehousing, massive media digitization, multimedia databases and Web technology

3 Databases are too big. Terrorbytes Data Rich but Information Poor Data Mining can help discover knowledge

4 We are not trying to find the needle in the hay stock because DBMSs know how to do that. We are merely trying to understand the consequences of the presence of the needle if it exists. What should we do?

5 What Led Us To This? Necessity is the Mother of Invention –Technology is available to help us collect data. Barcode, scanners, satellites, cameras, etc. –Technology is available to help us store data. Database, data warehouses, variety of responses –We are starving for knowledge (competitive edge, research, etc). We are swamped by data that continually pour on us. –We do not know what to do with this data. –We need to interpret this data in search for new knowledge.

6 Data What is our Need? Knowledge Extract interesting knowledge (rules, regularities, patterns, constraints) from data in large collections.

7 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

8 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

9 Data Collected (SOURCE) Business transactions Scientific data Medical and personal data Surveillance video and pictures Satellite sensing Games Data Collected (SOURCE) Business transactions Scientific data Medical and personal data Surveillance video and pictures Satellite sensing Games

10 Data Collected (Format) Digital Media CAD and Software Engineering Virtual Worlds Text Reports and Memos The World Wide Web Data Collected (Format) Digital Media CAD and Software Engineering Virtual Worlds Text Reports and Memos The World Wide Web

11 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

12 Process of non-trivial extraction of implicit, previously unknown and potentially useful information from large collections of data. Knowledge Discovery

13 Many Steps in KDD Process  Gathering the data together.  Cleanse the data and fit it together.  Select the necessary data.  Crunch and squeeze the data to extract the essence of it.  Evaluate the output and use it.

14 So What is Data Mining? In theory, Data Mining is a STEP in the knowledge discovery process. It is the extraction of implicit information from a large data set. In practice, data mining and knowledge discovery are becoming synonymous.

15 So What is Data Mining? There are other equivalent terms: KDD, knowledge extraction, discovery of regularities, patterns discovery, data archeology, data dredging, business intelligence, information harvesting Shouldn’t it be called knowledge mining instead of data mining?

16 Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process

17 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process

18 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Multiple data sources, often heterogeneous, may be combined in a common source

19 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Pattern Evaluation Data Mining: A KDD Process Noise data and irrelevant data are removed from the collections Data Mining

20 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Data relevant to the analysis is decided on and retrieved from the data collection

21 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Data Selection and Data transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Selected data is transformed into forms appropriate for the mining procedure

22 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Clever techniques are applied to extract patterns potentially useful

23 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Strictly interesting patterns representing knowledge are identified based on given measures

24 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation Data Mining: A KDD Process Knowledge representation: Uses visualization techniques to help users understand and interpret the data mining results

25 KDD is an Iterative Process

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27 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

28 What Kind of Data Can be Mined?  Flat Files  Heterogeneous and legacy databases  Relational databases And other DB: Object-oriented and object- relational data bases  Transactional databases Transaction(TID, Timestamp, {item1, items,..})

29 What Kind of Data Can be Mined? Data warehouses

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32 What Kind of Data Can be Mined? Multimedia Databases

33 What Kind of Data Can be Mined? Spatial Databases

34 What Kind of Data Can be Mined? Time-Series Databases

35 What Kind of Data Can be Mined? Text Documents

36 What Kind of Data Can be Mined? World Wide Web Components of WWW: 1.Content of the web 2.Structure of the web 3.Usage of the web Components of WWW: 1.Content of the web 2.Structure of the web 3.Usage of the web

37 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

38 What can be discovered? What can be discovered depends on the data mining task employed. Descriptive DM Describe general properties Predictive DM Tasks Infer on available data Descriptive DM Describe general properties Predictive DM Tasks Infer on available data

39 Data Mining Functionality  Characterization Summarization of general features of objects in a target class (concept description) Ex: Characterize graduate students in Science  Discrimination Comparison of general features of objects between a target class and a contrasting class (concept description) Ex: Compare students is Science and students in Arts

40 Data Mining Functionality  Association Studies the frequency of items occurring together in transactional databases Ex: buys(x, bread)  buys(x, milk)  Prediction Predicts some unknown or missing attribute values based on other information Ex: Forecast the sale value for next week based on available data.

41 Data Mining Functionality  Classification Organizes data in given classes based on attribute values (supervised classification) Ex: classify students based on final results  Clustering Organizes data in classes based on attribute values (unsupervised classification) Ex: group crime locations to find distribution patterns.

42 Data Mining Functionality  Outlier analysis Identifies and explains exceptions (surprises)  Time-series analysis: Analyzes trends and deviations, regression, sequential pattern, similar sequences

43 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

44 Is all that is Discovered Interesting? A data mining operation may generate thousands of patterns, not all of them are interesting. Data Mining results are sometimes so large that we may need to mine it too (Meta- Mining?) How to measure? INTERESTINGNESS

45 Interestingness Objective interestingness measures: –Based on statistics and structure of patterns, e.g., support, confidence, etc. Subjective interestingness measures: –Based on user’s beliefs in data, e.g., unexpectedness, novelty

46 Interestingness Measures: A pattern is interesting if it is: Easily understood by humans Valid on new or test data with some degree of certainty Potentially useful Novel, or validates some hypothesis that a user seeks to confirm

47 Introduction: Outline What kind of information are we collecting? What are Data Mining and Knowledge Discovery? What kind of data can be mined? What can be discovered? Is all that is discovered interesting and useful? Are there application examples?

48 Potential and/or Successful Applications Business data analysis and decision support –Marketing focalization Recognizing specific market segments that respond to particular characteristics Return on mailing campaign (target marketing) –Customer profiling Segmentation of customer for marketing strategies and/or product offerings Customer behavior understanding Customer retention and loyalty

49 Potential and/or Successful Applications Business data analysis and decision support –Market analysis and management Provide summary information for decision-making Market basket analysis, cross selling, market segmentation –Risk analysis and management What if analysis Forecasting Pricing analysis, competitive analysis Time-series analysis (ex. Stock market)

50 Potential and/or Successful Applications Fraud detection Detection of telephone fraud -Telephone call model: destination of the call, duration, time of the day or week. Analyze patterns that deviate from an expected norm. Detecting automotive and health insurance fraud Detection of credit-card fraud Detecting suspicious money transactions (money laundering)

51 Potential and/or Successful Applications Text Mining –Message filtering ( , newsgroups, etc) –Newspaper article analysis Medicine –Association pathology – symptoms –DNA

52 Potential and/or Successful Applications Sports –IBM Advanced Scout analyzed NBA game statistics (shots, blocks, fouls, assists) to gain competitive edge. Astronomy –JPL and Palomar Observatory discovered 22 quasars with the help of data mining –Identifying volcanoes on Jupiter

53 Potential and/or Successful Applications Surveillance cameras –Use of stereo cameras and outlier analysis to detect suspicious activities or individuals Web surfing and mining –IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages (e-commerce) –Adaptive web sites / improving Web site organization, etc.

54 Warning: Data Mining Should Not be Used Blindly! Data mining find regularities from history, but history is not the same as the future Association does not dictate trend or causality –Drink diet drinks lead to obesity

55 DBMS Transaction Subsystem Transaction manager Scheduler Recovery manager Buffer manager Systems manager Access manager File manager Transaction manager coordinates transactions on behalf of application programs It communicates with the scheduler The scheduler handles concurrency control. Its objective is to maximize concurrency without allowing simultaneous transactions to interfere with one another The recovery manager ensures that the database is restored to the right state before a failure occurred. The buffer manager is responsible for the transfer of data between disk storage and main memory.

56 Thank You


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