data mining


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data mining

  1. 1. Live Project on Data Mining
  2. 2. Objectives of the study <ul><li>To learn more about Data Mining. </li></ul><ul><li>To find out how does data mining works. </li></ul><ul><li>To find out what all soft wares are used for data mining. </li></ul><ul><li>To find out what all techniques are used in data mining. </li></ul><ul><li>To find out what are the uses of data mining. </li></ul>
  3. 3. Research Methodology <ul><li>Secondary data- From Internet, Books- Business Research Methods And Information Technology </li></ul><ul><li>  </li></ul>
  4. 4. Contents <ul><li>Introduction </li></ul><ul><li>Foundations of data mining </li></ul><ul><li>Steps in Evaluation of Data mining </li></ul><ul><li>Process of Data mining </li></ul><ul><li>Most commonly used techniques in Data mining </li></ul><ul><li>How Data mining works? </li></ul><ul><li>Soft wares used </li></ul><ul><li>Notable Uses of data mining </li></ul><ul><li>Privacy concern and ethics </li></ul><ul><li>Conclusion </li></ul>
  5. 5. What is Data Mining?
  6. 6. The Foundations of Data Mining
  7. 7. Steps in the Evaluation
  8. 8. Process of Data Mining
  9. 10. Most commonly used techniques in data mining <ul><li>Artificial neural networks : Non-linear predictive models that learn through training and resemble biological neural networks in structure. </li></ul><ul><li>Decision trees : Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . </li></ul><ul><li>Genetic algorithms : Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution. </li></ul><ul><li>Nearest neighbor method : A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique. </li></ul><ul><li>Rule induction : The extraction of useful if-then rules from data based on statistical significance . </li></ul>
  10. 12. How Data Mining Works <ul><li>The technique that is used to perform these feats in data mining is called modeling. </li></ul><ul><li>Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don't. </li></ul><ul><li>This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology. </li></ul><ul><li>Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer. </li></ul>
  11. 14. Soft wares used Software Name Data Mining Approaches ADAPA Predictive Analytics Classification Discovery, Cluster Discovery, Regression Discovery, Association Discovery Angoss Strategy BUILDER Cluster Discovery, Regression Discovery, Data Visualisation, Discovery Visualisation Data Detective Classification Discovery, Cluster Discovery, Association Discovery, Text Mining, Outlier Discovery, Data Visualisation, Discovery Visualisation
  12. 16. Notable Uses of data mining
  13. 17. Privacy concerns and ethics <ul><li>Data mining can uncover information or patterns which may compromise confidentiality and privacy obligations. </li></ul><ul><li>Through Data aggregation, is when the data which has been mined, possibly from various sources, has been put together so that it can be analyzed. </li></ul><ul><li>The threat to an individual's privacy comes into play when the data, once compiled, causes the data miner to be able to identify specific individuals, especially when originally the data was anonymous. </li></ul><ul><li>It is recommended that an individual is made aware of the following before data is collected: </li></ul><ul><li>The purpose </li></ul><ul><li>Data use </li></ul><ul><li>Who will be able to mine the data and use it </li></ul><ul><li>Data update. </li></ul>
  14. 18. Conclusion
  15. 19. Thank You