Data Mining     ADDBASE
What is data mining? The    process of extracting valid  previously unknown, comprehensive,  and actionable information f...
Data mining Applications It is almost applicable in all areas  whether it is for business or for  science. Provides    d...
Data mining ApplicationsRetail/Marketing Identify buying patterns of customers. Finding association among customer  demo...
Data mining ApplicationsBanking Detecting patterns of fraudulent credit  card use. Identifying loyal customers. Predict...
Data mining ApplicationsInsurance Claims analysis. Predicting which customers will buy  new policies.Medicine Character...
Data mining Operations4 main operations of data mining: Predictive modeling Database segmentation Link analysis Deviat...
Data mining Operations Predictive   modeling    Based observations to form a model of     the important characteristics ...
Data mining Operations Link   analysis    Based on links called associations     between the individual records and set ...
Data mining Process Cross-IndustryStandard Process for Data Mining (CRISP-DM)  Specifies a data of data mining process  ...
Data mining Process (cont…) Major objectives of this specification are  to make large data mining projects run  more effi...
Data mining Process (cont…) The  process is divided into 6 different  generic phases ranging from business  understanding...
Data mining Process (cont…)  Evaluation  Deployment Business    understanding    This phase is focuses on understandin...
Data mining Process (cont…)   Data preparation       This phase involves all the activities for        constructing the ...
Data mining Process (cont…)   Evaluation       This phase validates the model from the data        analysis point of vie...
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Data mining (prefinals)

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Data mining (prefinals)

  1. 1. Data Mining ADDBASE
  2. 2. What is data mining? The process of extracting valid previously unknown, comprehensive, and actionable information from large databases and using it to make crucial business decision It starts by developing a representation of simple data. then extended to larger sets of data working on the premise that the larger data has a structure similar to the
  3. 3. Data mining Applications It is almost applicable in all areas whether it is for business or for science. Provides different purpose and benefits depending where this technique is applied.
  4. 4. Data mining ApplicationsRetail/Marketing Identify buying patterns of customers. Finding association among customer demographic characteristic. Predicting response to mailing campaigns. Market basket analysis.
  5. 5. Data mining ApplicationsBanking Detecting patterns of fraudulent credit card use. Identifying loyal customers. Predicting customers likely to change their credit card affiliation. Determining credit card spending by customer groups.
  6. 6. Data mining ApplicationsInsurance Claims analysis. Predicting which customers will buy new policies.Medicine Characterizing patient behavior to predict surgery visit. Identifying successful medical therapies for different illnesses.
  7. 7. Data mining Operations4 main operations of data mining: Predictive modeling Database segmentation Link analysis Deviation detection
  8. 8. Data mining Operations Predictive modeling  Based observations to form a model of the important characteristics of some phenomenon. Database segmentation  Is about partitioning of database into an unknown number of segments or clusters of similar records.
  9. 9. Data mining Operations Link analysis  Based on links called associations between the individual records and set of records in a database. Deviation detection  Newest data mining operation  Often a source of true discovery because it identifies outliers which express deviation.
  10. 10. Data mining Process Cross-IndustryStandard Process for Data Mining (CRISP-DM)  Specifies a data of data mining process model that is not specific to any industry tool.  Involved from unknown knowledge discovery processes used widely in industry and in direct response to user requirements.
  11. 11. Data mining Process (cont…) Major objectives of this specification are to make large data mining projects run more efficiently as well as to make them cheaper, more reliable and more manageable. A hierarchy process model
  12. 12. Data mining Process (cont…) The process is divided into 6 different generic phases ranging from business understanding to deployment of project result. The phases of CRISP-DM model are:  Business understanding  Data understanding  Data preparation  Modeling
  13. 13. Data mining Process (cont…)  Evaluation  Deployment Business understanding  This phase is focuses on understanding the project objectives and requirements from the business point of view. Data understanding  This phase includes task for initial collection of the data and is concerned with establishing the main characteristics
  14. 14. Data mining Process (cont…) Data preparation  This phase involves all the activities for constructing the final data set on which modeling tools can be applied directly. Modeling  This phase is the actual data mining operation and involves selecting modeling techniques, selecting modeling parameters and assessing the model created.
  15. 15. Data mining Process (cont…) Evaluation  This phase validates the model from the data analysis point of view.  The model and the steps in modeling are verified within the context of achieving the business goals. Deployment  This phase is all about generating report or as complex as implementing repeatable data mining processing across the enterprise.
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