Unit 1
- 1. COMPUTER ORGANIZATION AND ARCHITECTURE Prepared By KAVITHA JACKLEEN.I Asst. Professor CSE Dept.
- 2. Unit-I 2January 6, 2018 Data Warwhousing and Data Mining
- 3. January 6, 2018 Data Mining: Concepts and Techniques 3 Introduction • Motivation: Why data mining? • What is data mining? • Data Mining: On what kind of data? • Data mining functionality • Classification of data mining systems • Top-10 most popular data mining algorithms • Major issues in data mining • Overview of the course
- 4. January 6, 2018 Data Mining: Concepts and Techniques 4 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 sets
- 5. January 6, 2018 Data Mining: Concepts and Techniques 5 Evolution of Sciences • Before 1600, empirical science • 1600-1950s, theoretical science – Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. • 1950s-1990s, computational science – Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.) – Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models. • 1990-now, data science – The flood of data from new scientific instruments and simulations – The ability to economically store and manage petabytes of data online – The Internet and computing Grid that makes all these archives universally accessible – Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge! • Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002
- 6. January 6, 2018 Data Mining: Concepts and Techniques 6 Evolution of Database Technology • 1960s: – Data collection, database creation, IMS and network DBMS • 1970s: – Relational data model, relational DBMS implementation • 1980s: – RDBMS, advanced data models (extended-relational, OO, deductive, etc.) – Application-oriented DBMS (spatial, scientific, engineering, etc.) • 1990s: – Data mining, data warehousing, multimedia databases, and Web databases • 2000s – Stream data management and mining – Data mining and its applications – Web technology (XML, data integration) and global information systems
- 7. January 6, 2018 Data Warwhousing and Data Mining 7
- 8. January 6, 2018 Data Mining: Concepts and Techniques 8 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 – Data mining: a misnomer? • 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
- 9. January 6, 2018 Data Mining: Concepts and Techniques 9 Knowledge Discovery (KDD) Process – Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
- 10. January 6, 2018 Data Mining: Concepts and Techniques 10 Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems
- 11. January 6, 2018 Data Mining: Concepts and Techniques 11 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
- 12. January 6, 2018 Data Mining: Concepts and Techniques 12 Why Not Traditional Data Analysis? • Tremendous amount of data – Algorithms must be highly scalable to handle such as tera-bytes of data • High-dimensionality of data – Micro-array may have tens of thousands of dimensions • High complexity of data – Data streams and sensor data – Time-series data, temporal data, sequence data – Structure data, graphs, social networks and multi-linked data – Heterogeneous databases and legacy databases – Spatial, spatiotemporal, multimedia, text and Web data – Software programs, scientific simulations • New and sophisticated applications