introduction to kdd. evolution of database technology 1950s: first computers, use of computers for...
TRANSCRIPT
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Introduction to KDD
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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
implementation1980s: 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
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Databases are too big.
Terrorbytes
Data Rich but Information Poor
Data Mining can help discover knowledge
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• 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?
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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.
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Data
What is our Need?
Knowledge
Extract interesting knowledge (rules, regularities, patterns, constraints) from data in large collections.
Extract interesting knowledge (rules, regularities, patterns, constraints) from data in large collections.
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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?
![Page 8: Introduction to KDD. Evolution of Database Technology 1950s: First computers, use of computers for census. 1960s: Data collection, database creation](https://reader036.vdocument.in/reader036/viewer/2022062318/551af0825503462e578b4966/html5/thumbnails/8.jpg)
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?
![Page 9: Introduction to KDD. Evolution of Database Technology 1950s: First computers, use of computers for census. 1960s: Data collection, database creation](https://reader036.vdocument.in/reader036/viewer/2022062318/551af0825503462e578b4966/html5/thumbnails/9.jpg)
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
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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
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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?
![Page 12: Introduction to KDD. Evolution of Database Technology 1950s: First computers, use of computers for census. 1960s: Data collection, database creation](https://reader036.vdocument.in/reader036/viewer/2022062318/551af0825503462e578b4966/html5/thumbnails/12.jpg)
Process of non-trivial extraction of implicit, previously unknown and potentially useful information from large collections of data.
Process of non-trivial extraction of implicit, previously unknown and potentially useful information from large collections of data.
Knowledge Discovery
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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.
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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.
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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?
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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
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD Process
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Data Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessMultiple data sources, often heterogeneous, may be combined in a common source
Multiple data sources, often heterogeneous, may be combined in a common source
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection and transformation
Pattern Evaluation
Data Mining: A KDD ProcessNoise data and irrelevant data are removed from the collections
Noise data and irrelevant data are removed from the collections
Data Mining
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Data Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessData relevant to the analysis is decided on and retrieved from the data collection
Data relevant to the analysis is decided on and retrieved from the data collection
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Data Selection and Data transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessSelected data is transformed into forms appropriate for the mining procedure
Selected data is transformed into forms appropriate for the mining procedure
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessClever techniques are applied to extract patterns potentially useful
Clever techniques are applied to extract patterns potentially useful
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessStrictly interesting patterns representing knowledge are identified based on given measures
Strictly interesting patterns representing knowledge are identified based on given measures
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Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection and transformation
Data Mining
Pattern Evaluation
Data Mining: A KDD ProcessKnowledge representation: Uses visualization techniques to help users understand and interpret the data mining results
Knowledge representation: Uses visualization techniques to help users understand and interpret the data mining results
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KDD is an Iterative Process
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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?
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What Kind of Data Can be Mined?
Flat FilesHeterogeneous and legacy databasesRelational databases
And other DB: Object-oriented and object-relational data bases
Transactional databasesTransaction(TID, Timestamp, {item1, items,..})
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What Kind of Data Can be Mined?
Data warehouses
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What Kind of Data Can be Mined?
Multimedia Databases
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What Kind of Data Can be Mined?
Spatial Databases
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What Kind of Data Can be Mined?
Time-Series Databases
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What Kind of Data Can be Mined?
Text Documents
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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
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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?
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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
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Data Mining Functionality
CharacterizationSummarization of general features of objects in a
target class (concept description)Ex: Characterize graduate students in Science
DiscriminationComparison of general features of objects
between a target class and a contrasting class (concept description)
Ex: Compare students is Science and students in Arts
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Data Mining Functionality
AssociationStudies the frequency of items occurring together
in transactional databasesEx: buys(x, bread) buys(x, milk)
PredictionPredicts some unknown or missing attribute
values based on other informationEx: Forecast the sale value for next week based
on available data.
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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.
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Data Mining Functionality
Outlier analysis
Identifies and explains exceptions (surprises)
Time-series analysis:
Analyzes trends and deviations, regression, sequential pattern, similar sequences
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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?
![Page 44: Introduction to KDD. Evolution of Database Technology 1950s: First computers, use of computers for census. 1960s: Data collection, database creation](https://reader036.vdocument.in/reader036/viewer/2022062318/551af0825503462e578b4966/html5/thumbnails/44.jpg)
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
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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
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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
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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?
![Page 48: Introduction to KDD. Evolution of Database Technology 1950s: First computers, use of computers for census. 1960s: Data collection, database creation](https://reader036.vdocument.in/reader036/viewer/2022062318/551af0825503462e578b4966/html5/thumbnails/48.jpg)
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
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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)
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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)
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Potential and/or Successful Applications
• Text Mining– Message filtering (e-mail, newsgroups, etc)– Newspaper article analysis
• Medicine– Association pathology – symptoms– DNA
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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
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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.
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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
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DBMS Transaction SubsystemTransaction
managerScheduler
Recoverymanager
Buffermanager
Systemsmanager
Accessmanager
Filemanager
• 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.
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Thank You