Data mining


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Data mining

  1. 1. V.Loganayagi@Abinaya. K.P.Unnamalai. computer science Iii year(I Shift)
  2. 2.  Data mining an interdisciplinary subfield of computer science is the computational process of discovering patterns in large datasets. Involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Data mining process is to extract information from a data set and transform it into an understandable structure for further use.
  3. 3.  There are a number of applications that data mining has. Banking: Loan/credit card approval Predict good customers based on old customers Customer relationship management: Identify those who are likely to leave for a competitor. Targeted marketing: Identify likely responders to promotions Fraud detection: Telecommunications, financial transactions From an online stream of event identify fraudulent events
  4. 4.  Manufacturing and production: Automatically adjust knobs when process parameter changes Medicine: disease outcome, effectiveness of treatments Analyze patient disease history: find relationship between diseases Molecular/Pharmaceutical: Identify new drugs Scientific data analysis: Identify new galaxies by searching for sub clusters Web site/store design and promotion: Find affinity of visitor to pages and modify layout
  5. 5.  The term Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the "high-level" application of particular data mining methods. It is of interest to researchers in machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, and data visualization. The unifying goal of the KDD process is to extract knowledge from data in the context of large databases.
  6. 6.  Several core techniques that are used in data mining describe the type of mining and data recovery operation. Unfortunately, the different companies and solutions do not always share terms, which can add to the confusion and apparent complexity. Lets look at some key techniques and examples of how to use different tools to build the data mining.
  7. 7. Clustering
  8. 8.  Data mining itself relies upon building a suitable data model and structure that can be used to process, identify, and build the information that you need. Regardless of the source data form and structure, structure and organize the information in a format that allows the data mining to take place in as efficient a model as possible. Depending on your data source, how you build and translate this information is an important step, regardless of the technique you use to finally analyze the data
  9. 9. Marking/Retailing: Data mining can aid direct marketers by providing them with useful and accurate trends about their customers’ purchasing behavior. Based on these trends, marketers can direct their marketing attentions to their customers with more precision. For example, marketers of a software company may advertise about their new software to consumers who have a lot of software purchasing history. In addition, data mining may also help marketers in predicting which products their customers may be interested in buying.
  10. 10. Banking/Crediting: Data mining can assist financial institutions in areas such as credit reporting and loan information. For example, by examining previous customers with similar attributes, a bank can estimated the level of risk associated with each given loan. In addition, data mining can also assist credit card issuers in detecting potentially fraudulent credit card transaction. Although the data mining technique is not a 100% accurate in its prediction about fraudulent charges, it does help the credit card issuers reduce their losses.
  11. 11. Law enforcement: Data mining can aid law enforcers in identifying criminal suspects as well as apprehending these criminals by examining trends in location, crime type, habit, and other patterns of behaviors.Researchers: Data mining can assist researchers by speeding up their data analyzing process; thus, allowing them more time to work on other projects.
  12. 12. Privacy Issues: Personal privacy has always been a major concern in this country. In recent years, with the widespread use of Internet, the concerns about privacy have increase tremendously. Because of the privacy issues, some people do not shop on Internet. Although it is against the law to sell or trade personal information between different organizations, selling personal information have occurred. The selling of personal information may also bring harm to these customers because you do not know what the other companies are planning to do with the personal information that they have purchased.
  13. 13. Security issues: Although companies have a lot of personal information about us available online, they do not have sufficient security systems in place to protect that information. This incidence illustrated that companies are willing to disclose and share your personal information, but they are not taking care of the information properly. With so much personal information available, identity theft could become a real problem.
  14. 14. Misuse of information/inaccurate information: Trends obtain through data mining intended to be used for marketing purpose or for some other ethical purposes, may be misused. Unethical businesses or people may used the information obtained through data mining to take advantage of vulnerable people or discriminated against a certain group of people. In addition, data mining technique is not a 100 percent accurate; thus mistakes do happen which can have serious consequence.
  15. 15. “Delivering results that endure just what you needed”