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Alisha Korpal
Nivia Jain
Sharuti Jain
Data Mining ?
Huge amounts of data Electronic record of our decisions
Choices in the supermarket Financial records
Data vs. Information
Data : Collection of raw data ,
facts and figures.
Information: processed form of data
Data Mining Extracting or “mining” knowledge from large amounts of
data Data – driven discovery and modeling of hidden patterns
in large volumes of data Extraction of interesting (non trivial, implicit, previously
and potentially useful) information or patterns from data
in large databases.
Data Mining Process Defining the problem Preparing data Exploring data Building Models Exploring and validating Models Deploying and Updating models
Data Mining Process
Defining the Problem What are you looking for?
What types of relationships are you trying to find?
Do you want to make predictions from the data mining model, or just look for interesting patterns and associations?
Contd…
Which attribute of the dataset do you want to try to predict?
How are the columns related? If there are multiple tables, how are the tables related?
Does the problem you are trying to solve reflect the policies or processes of the business?
Preparing Data
Exploring Data
You must understand the data in order to make appropriate decisions when you create the mining models. Exploration techniques include calculating the minimum and maximum values, calculating mean and standard deviations, and looking at the distribution of the data.
Models
Building Models Exploring and Validating Models Deploying and Updating Models
Evolution of Data Mining Data collection -1960s
Data access - 1980s Data Warehousing & decision support -1990s Data Mining -Emerging Today
Evolutionary Step
Business Question Enabling Technologies
Characteristics
Data Collection(1960s)
"What was my total revenue in the last five years?"
Computers, tapes, disks Retrospective, static data delivery
Data Access(1980s)
"What were unit sales in New England last March?"
Relational databases (RDBMS), Structured Query Language (SQL), ODBC
Retrospective, dynamic data delivery at record level
Data Warehousing &Decision Support(1990s)
"What were unit sales in New England last March? Drill down to Boston."
On-line analytic processing (OLAP), multidimensional databases, data warehouses
Retrospective, dynamic data delivery at multiple levels
Data Mining(Emerging Today)
"What’s likely to happen to Boston unit sales next month? Why?"
Advanced algorithms, multiprocessor computers, massive databases
Prospective, proactive information delivery
Data mining Vs OLAP
On-line Analytical Processing Provides you with a very good
view of what is happening, but can not predict what will happen in the future or why it is happening
Scope of Data Mining
Automated prediction of trends and behaviors Automated discovery of previously unknown
patterns
Applications
Science: Chemistry, Physics, Medicine Biochemical analysis Remote sensors on a satellite Medical images analysis
Applications Financial Industry, Banks, Businesses, E
commerce Stock and investment analysis Risk management Sales forecasting
Applications
Database analysis and decision support Market analysis and management
Target marketing, customer relation management, market basket analysis, cross selling
Applications
Risk analysis and management Forecasting, customer retention, improved underwriting Fraud detection and management
References: http://www.data-miners.com/resources/SUGI29-Survival.
pdf http://docs.google.com/viewer?
a=v&q=cache:VRsb5lbwpGoJ:www.sdsc.edu/us/training/workshops/2006cihass/docs/2006cihass_DataMiningIntro.ppt+applications+of+data+mining+ppt&hl=en&gl=in&pid=bl&srcid=ADGEESg5iQeaEGa0RoHJpbQyDDbVKPNJwOS3Zg71DTIgFf8PhSbzZ39oAdQNwPb8wvwJAbwFwp-HcAwhGF-9C6TiHM3pv7vQm7Xf8umeBDY_oG6VtzK8eVwqAo95evUgkcvWwDO5YwKT&sig=AHIEtbQ1bj7uPnVGzCNysOs5V7_5apQk0A&pli=1
References:
http://www.thearling.com/text/dmwhite/dmwhite.htm http://www.anderson.ucla.edu/faculty/jason.frand/
teacher/technologies/palace/datamining.htm http://msdn.microsoft.com/en-us/library/ms174949.aspx
Result of Data Mining
What may happen in future Classifying people or things into groups by recognizing
patterns Clustering people or things into groups based on their
attributes Sequencing what events are likely to lead to later events
Data Mining is not
“Blind” applications of algorithms Going to find relations where none exist Presenting data in different ways A difficult to understand technology requiring an
advanced degree in computer science