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Note for Data Mining And Data Warehousing - DMDW by nainish aggarwal

  • Data Mining And Data Warehousing - DMDW
  • Note
  • Dr. A.P.J. Abdul Kalam Technical University - AKTU
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Nainish Aggarwal
Nainish Aggarwal
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Why Data Mining? • The Explosive Growth of Data: from terabytes to petabytes – Data collection and data availability • Automated data collection tools, database systems, Web, computerized society – Major sources of abundant data • Business: Web, e-commerce, transactions, stocks, … • Science: Remote sensing, bioinformatics, scientific simulation, … • Society and everyone: news, digital cameras, YouTube • We are drowning in data, but starving for knowledge! • “Necessity is the mother of invention”—Data mining—Automated analysis of massive data

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What Is Data Mining? • Data mining (knowledge discovery from data) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data • Alternative names – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. • Watch out: Is everything “data mining”? – Simple search and query processing – (Deductive) expert systems 2

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Data Mining in Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA 3

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Lecture Notes