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Business Intelligence

Business Intelligence. Data Mining. Main Agenda. Concepts and Application Techniques and tools. What is Data Mining. Data mining concepts and techniques aims at uncovering interesting data patterns hidden in large data sets .

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Business Intelligence

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  1. Business Intelligence Data Mining

  2. Main Agenda • Concepts and Application • Techniques and tools

  3. What is Data Mining • Data mining concepts and techniques aims at uncovering interesting data patterns hidden in large data sets. • The information and knowledge gained from data mining can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration.

  4. History of Data Mining • Data mining can be viewed as a result of the natural evolution of information technology. • The database system industry has witnessed an evolutionary path in the development of the following functionalities. • data collection and database creation, data management (including data storage and retrieval, and database transaction processing)

  5. History of Data Mining • It also includes advanced data analysis (involving data warehousing and data mining). • The steady and amazing progress of computer hardware technology in the past three decades has led to large supplies of powerful and affordable computers, data collection equipment, and storage media.

  6. History of Data Mining • This technology provides a great boost to the database and information industry, and makes a huge number of databases and information repositories available for transaction management, information retrieval, and data analysis. • This led to the emergence of Data Warehouse technology.

  7. History of Data Mining • Data warehouse technology such as on-line analytical processing (OLAP) focuses on analysis techniques such as summarization, consolidation, and aggregation as well as the ability to view information from different angles.

  8. History of Data Mining • Although OLAP tools support multidimensional analysis and decision making, additional data analysis tools are required for in-depth analysis, such as data classification and clustering. • Also huge amounts of data is accumulated beyond database and data warehouses such as www and data streams. • The effective and efficient analysis of data in such different forms become challenging task

  9. History of Data Mining • The abundance of data, coupled with the need for powerful data analysis tools, has been described as a data rich but information poor situation.

  10. History of Data Mining • We are data rich, but information poor

  11. History of Data Mining • All of the above reasons lead to the invention of Data Mining. • And the major focus of data mining tools is to perform data analysis and may uncover important data patterns, and contributing greatly to business strategy and scientific and medical research.

  12. So, What Is Data Mining? • Simply stated, data mining refers to extracting or “mining” knowledge from large amounts of data. • The term is actually a misnomer. • Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining.

  13. So, What Is Data Mining? • Thus, data mining should have been more appropriately named “knowledge mining from data”, which is unfortunately somewhat long. • “Knowledge mining,” a shorter term, may not reflect the emphasis on mining from large amounts of data. • Thus, such a misnomer that carries both “data” and “mining” became a popular choice.

  14. So, What Is Data Mining? • Many other terms carry a similar or slightly different meaning to data mining, such as knowledge mining from data, knowledge extraction, pattern analysis, data archaeology, and data dredging. • Many people treat data mining as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD.

  15. So, What Is Data Mining? • Simply put (standard definition); Data mining is the process of discovering interesting knowledge from large amounts of data stored in databases, data warehouses, or other information repositories.

  16. Architecture of DM system • Based on this definition, the architecture of a typical data mining system may have the following major components:- • Database, data warehouse, World Wide Web, or other information repository: This is one or a set of databases, data warehouses, spreadsheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data.

  17. Architecture of DM system • Database or data warehouse server: The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.

  18. Data Mining Tasks • Data Mining Tasks can be classified into two categories: • Descriptive • Predictive. • Descriptive mining tasks characterize the general properties of the data in the database. • Predictive mining tasks perform inference on the current data in order to make predictions.

  19. Concept/Class Description • Data can be associated with classes or concepts. • For example, in the AllElectronics store, classes of items for sale include computers and printers, and concepts of customers include bigSpenders and budgetSpenders.

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