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"Data Mining"

"Data Mining"






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  • Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results
  • Knowledge may be recorded in an individual brain or stored in documents, organizational processes, products, facilities and systems
  • CRM  By applying CRM analytics to your marketing strategies, you will gain a comprehensive view of your customers and target the allocation of your marketing resources FORECASTING 

"Data Mining" "Data Mining" Presentation Transcript

  • DATA MINING Presented by Kalyan K Beemanapalli Aditya K Grandhi
  • Outline
    • Introduction, Definition, Technology Overview
    • A Small Example
    • Applications
    • Future Applications
    • Business Value
    • Issues and Concerns
    • References
  • Definition, Introduction
    • History of Data Mining
    • An information extraction activity whose goal is to discover hidden facts contained in databases.
    • Statistical Analysis Vs Data Mining
    • Data mining, in many ways, is fundamentally the adaptation of machine learning techniques to business applications.
  • Overview
  • Technology Overview
    • Association : If a customer buys snacks, there is a 85% probability that the customer will also buy soft drinks or beer.
    • Classification : Classifications look at the behavior and attributes of already determined groups
    • Sequence : a person who buys a washing machine may also buy a clothes dryer within six months with a probability of 0.7
    • Clustering : Clustering divides items into groups based on the similarities the data mining tool finds
  • Market Basket Analysis
    • 80% of the people who buy milk also buy bread
    • On Friday’s, 70% of the men who bought diapers also bought beer.
    • What is the relationship between diapers and beer?
    • Walmart could trace the reason after doing a small survey!
  • Role of Domain Knowledge
    • Informal knowledge about application
    • domain can be formalized into a set of
    • Domain Knowledge Elements
    • What is Domain Knowledge?
    • Why Domain Knowledge is relevant in
    • the case of Data Mining?
    •  The present algorithms just mine the data and
    • ask domain experts to decide the
    • interestingness of the results.
    •  Can Data mining algorithms be improved to
    • use the knowledge possessed by
    • domain experts?
  • Applications of Data Mining
    • CRM - allow you to determine who your
    • best customers are and why.
    • Forecasting - Forecasting tools allow users to collect data, find patterns in that data and then predict future events or behaviors
    • Online Fraud Detection
    • e-Business Intelligence
    • The list is not exhaustive……….
  • Future Applications
    • Network Intrusion Detection
    • Researchers in Computer Science are concentrating on including the domain knowledge into the data mining algorithms
    • Example: Exploring the HR domain in an organization
  • Business Value
    • Venkataraman’s Architecture: This technology has the potential to affect the Business Scope of an organization
    • Amazon’s case :
    •  20% for advertisement; 80% for customer experience
  • Issues and Concerns
    • Privacy Concerns : In the light of developments in technology to analyze personal data, public concerns regarding privacy are rising – PPDM workshop
    •  Example : An insurance can decide its policy just by knowing your social security number
    • Security,Ethical and legal issues: An individual country's legal system may prevent sharing of customer data between a subsidiary and its parent
    • Interesting Observation: How do you quantify Privacy?
  • References
    • http://www.aaai.org/AITopics/html/ethics.html
    • http://businessintelligence.ittoolbox.com/
    • http://www.ipc.on.ca/docs/datamine.pdf
    • http://www.centeronline.org/knowledge/article.cfm?ID=843&ContentProfileID=123374&Action=searching
  • Questions?