2. Content
Data Mining
Data Mining Definition
Data Mining – Two Main Components
Data Mining vs. Data Analysis
What is (not) Data Mining?
Related Fields
Data Mining Process
Major Data Mining Tasks
Uses of Data Mining
Sources of Data for Mining
Challenges of Data Mining
Advantages
Conclusion
Reference
3. Data Mining
New buzzword, old idea.
Inferring new information from already collected
data.
Traditionally job of Data Analysts
Computers have changed this.
Far more efficient to comb through data using a
machine than eyeballing statistical data.
4. Data Mining Definition
Data mining in Data is the
non-trivial process of identifying
valid
novel
potentially useful
and ultimately understandable patterns in data.
5. Data Mining vs. Data
Analysis
In terms of software and the marketing thereof
Data Mining != Data Analysis
Data Mining implies software uses some intelligence
over simple grouping and partitioning of data to infer
new information.
Data Analysis is more in line with standard statistical
software (ie: web stats). These usually present
information about subsets and relations within the
recorded data set (ie: browser/search engine usage,
average visit time, etc. )
6. What is (not) Data Mining?
Look up phone number
in phone directory
Query a Web search
engine for information
about “Amazon”
•Certain names are more
prevalent in certain US
locations (O’Brien,
O’Rurke, O’Reilly… in
Boston area)
• Group together similar
documents returned by
search engine according to
their context (e.g. Amazon
rainforest, Amazon.com,)
What is not Data Mining? What is Data Mining?
8. Why Mine Data? Scientific
Viewpoint
Data collected and stored at
enormous speeds (GB/hour)
o remote sensors on a satellite
o telescopes scanning the skies
o microarrays generating gene
expression data
o scientific simulations
generating terabytes of data
Traditional techniques infeasible for raw data
Data mining may help scientists
o in classifying and segmenting data
o in Hypothesis Formation
12. Major Data Mining Tasks
Classification: predicting an item class
Associations: e.g. A & B & C occur frequently
Visualization: to facilitate human discovery
Estimation: predicting a continuous value
Deviation Detection: finding changes
Link Analysis: finding relationships...
13. Uses of Data Mining
AI/Machine Learning
Combinatorial/Game Data Mining
Good for analyzing winning strategies to games, and thus
developing intelligent AI opponents. (ie: Chess)
Business Strategies
Market Basket Analysis
Identify customer demographics, preferences, and
purchasing patterns.
Risk Analysis
Product Defect Analysis
Analyze product defect rates for given plants and predict
possible complications (read: lawsuits) down the line.
14. Uses of Data Mining (Cont..)
User Behavior Validation
Fraud Detection
In the realm of cell phones
Comparing phone activity to calling records. Can
help detect calls made on cloned phones.
Similarly, with credit cards, comparing purchases
with historical purchases. Can detect activity with
stolen cards.
15. Uses of Data Mining (Cont..)
Health and Science
Protein Folding
Predicting protein interactions and functionality
within biological cells. Applications of this research
include determining causes and possible cures for
Alzheimers, Parkinson's, and some cancers (caused
by protein "misfolds")
Extra-Terrestrial Intelligence
Scanning Satellite receptions for possible
transmissions from other planets.
For more information see Stanford’s Folding@home
and SETI@home projects. Both involve participation
in a widely distributed computer application.
16. Sources of Data for Mining
Databases (most obvious)
Text Documents
Computer Simulations
Social Networks
17. Advantages of Data Mining
Marketing / Retail
Finance / Banking
Manufacturing
Governments
18. Challenges of Data Mining
Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
19. Conclusion
Comprehensive data warehouses that integrate operational
data with customer, supplier, and market information have
resulted in an explosion of information.
Competition requires timely and sophisticated analysis on an
integrated view of the data.
However, there is a growing gap between more powerful
storage and retrieval systems and the users’ ability to
effectively analyze and act on the information they contain.