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Chapter-1

Chapter-1. Data Mining This work is created by Dr. Anamika Bhargava, Ms. Pooja Kaul , Ms. Priti Bali and Ms. Rajnipriya Dhawan and licensed under a Creative Commons Attribution 4.0 International License. Data Mining. “ Necessity is the mother of invention.” —Plato

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Chapter-1

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  1. Chapter-1 Data Mining This work is created by Dr. Anamika Bhargava, Ms. Pooja Kaul , Ms. Priti Bali and Ms. Rajnipriya Dhawan and licensed under a Creative Commons Attribution 4.0 International License.

  2. Data Mining “Necessity is the mother of invention.” —Plato Data mining refers to as extracting or mining knowledge from large amounts of data. Data mining should have been more appropriately named "knowledge mining from data" which is a long term , and "Knowledge mining" isa shorter term but it does not emphasis on mining from large amounts of data. • Many people treat data mining as a synonym of, KDD ( Knowledge Discovery from Data).

  3. Question arises why data mining is required? • What Motivated Data Mining?The wide availability of huge amounts of data and the need for turning such data into useful information and knowledge , motivated data mining. • Why Is It Important? The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, customer retention, to production control and science exploration.

  4. Evolutionary Path of Data Mining Data mining can be viewed as a result of the natural evolution of information technology. An evolutionary path in the development of the following functionalities having • Data collection and database creation, • Data management(data storage ,retrieval & transaction processing), • Advanced data analysis(data warehousing and data mining).

  5. Stages of Data Mining • The early development of data mining is data collection and database creation . • Next level is later development of effective mechanisms for data storage ,retrieval, query and transaction processing. • Data can be stored in many different kinds of databases and information repositories. One data repository architecture is the data warehouse .

  6. Data warehouse is a repository of multiple heterogeneous data sources organized under a unified schema at a single site in order to facilitate management decision making. • Data warehouse technology includes data cleaning, data integration, and on-line analytical processing (OLAP).

  7. Data mining as a step in the process of knowledge discovery.

  8. Essential step in the process of knowledge discovery • Data cleaning (to remove noise and inconsistent data) • Data integration (where multiple data sources may be combined) into one. • Data selection (where data relevant to the analysis task are retrieved from the database) • Data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations)

  9. Data mining (an essential process where intelligent methods are applied in order to extract data patterns) • Pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures. • Knowledge presentation (where visualization and knowledge representation techniques are used to present the mined knowledge to the user)

  10. Thank you

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