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PRESENTED BY:
Namitha sudhakaran
School of management
NIT-Warangal
 Lots of collected and warehoused data being
 Computer have become cheaper and more powerful
 Competitive pressure
 Data explosion problem
 Starving for knowledge
 Solution is data mining
 Extraction of implicit, previously unknown and
potentially useful information from data
 Exploration and analysis by automatic or semi
automatic means of large quantity of data
to discover meaning full pattern
 What is data mining?
Data mining is knowledge discovery, extraction of
interesting non trivial, implicit, previously unknown
and useful information or pattern.
 what is not data mining?
 Expert system or statistical programs
 Deductive query processing
 Data base to be mined.
Relational, transactional, object oriented, time series,
etc..
 Knowledge to be mined
Characterization, clustering, discrimination,
association, trend etc..
 Techniques utilized
Data base oriented, data ware house(OLAP),machine
learning, statistics, neural network etc
 Applications adapted
Retail, telecommunication ,banking, fraud detection,
market analysis etc
prediction task
 Classification
 Regression
 Deviation detection
Description task
 Clustering
 Association rule
 Discovery
 Sequential pattern discovery
 Data analysis and decision support
 Market analysis
 Risk analysis
 Fraud detection
 Text mining
 Intelligent query answering
 Sports
 Astronomy
 Characterization and discrimination
 Association
 Classification and prediction
 Cluster analysis
 Outlier analysis
 Trend and evaluation analysis
 Statistical analysis
 Mining methodology and user interaction
 Performance and scalability
 Issues relating to diversity of data types
 Issues related to application and social impact
 U.M. Fayyad, G.Shapiro,p.Smyth advances in
knowledgediscovery and datamining aaai/mit press1996
 J.Han and m.Kamber datamining:concepts nad
techniques.Morgan kaufmann ,2000
 https://www.simplilearn.com/data-mining.
 http://searchsqlserver.techtarget.com/definition/data-mart.

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Bi

  • 1. PRESENTED BY: Namitha sudhakaran School of management NIT-Warangal
  • 2.  Lots of collected and warehoused data being  Computer have become cheaper and more powerful  Competitive pressure  Data explosion problem  Starving for knowledge  Solution is data mining
  • 3.  Extraction of implicit, previously unknown and potentially useful information from data  Exploration and analysis by automatic or semi automatic means of large quantity of data to discover meaning full pattern
  • 4.  What is data mining? Data mining is knowledge discovery, extraction of interesting non trivial, implicit, previously unknown and useful information or pattern.  what is not data mining?  Expert system or statistical programs  Deductive query processing
  • 5.  Data base to be mined. Relational, transactional, object oriented, time series, etc..  Knowledge to be mined Characterization, clustering, discrimination, association, trend etc..  Techniques utilized Data base oriented, data ware house(OLAP),machine learning, statistics, neural network etc  Applications adapted Retail, telecommunication ,banking, fraud detection, market analysis etc
  • 6. prediction task  Classification  Regression  Deviation detection Description task  Clustering  Association rule  Discovery  Sequential pattern discovery
  • 7.  Data analysis and decision support  Market analysis  Risk analysis  Fraud detection  Text mining  Intelligent query answering  Sports  Astronomy
  • 8.
  • 9.
  • 10.
  • 11.  Characterization and discrimination  Association  Classification and prediction  Cluster analysis  Outlier analysis  Trend and evaluation analysis  Statistical analysis
  • 12.  Mining methodology and user interaction  Performance and scalability  Issues relating to diversity of data types  Issues related to application and social impact
  • 13.  U.M. Fayyad, G.Shapiro,p.Smyth advances in knowledgediscovery and datamining aaai/mit press1996  J.Han and m.Kamber datamining:concepts nad techniques.Morgan kaufmann ,2000  https://www.simplilearn.com/data-mining.  http://searchsqlserver.techtarget.com/definition/data-mart.