PPT-Introduction to Data Mining and Classification

F Michael Speed PhD Analytical Consultant SAS Global Academic Program Objectives State one of the major principles underlying data mining Give a high level overview of three classification procedures. Data Mining/Machine Learning Algorithms for Business Intelligence. Dr. Bambang Parmanto. Extraction Of Knowledge From Data. DSS Architecture: Learning and Predicting. Courtesy: Tim Graettinger. Data Mining: Definitions. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. . Basic Concepts. . Decision Trees. . Evaluation. . Nearest-Neighbor Classifier. What is a hipster?. Examples of hipster look. A hipster is defined by facial hair. Hipster or Hippie?. Facial hair alone is not enough to characterize hipsters. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). 12-. 1. Data mining is a rapidly growing field of business analytics focused on better understanding of characteristics and patterns among variables in large data sets.. It is used to identify and understand hidden patterns that large data sets may contain.. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall. 12-. 1. The Scope of Data Mining. Data Exploration and Reduction. Classification. Classification Techniques. Association Rule Mining. Instructor: . Yizhou. Sun. yzsun@ccs.neu.edu. January 6, 2013. Chapter 1. : Introduction. Course Information. Class . homepage: . http://. www.ccs.neu.edu/home/yzsun/classes/2013Spring_CS6220/index.htm. Dosen: Ariesta Damayanti. Email: riestamaya@gmail.com. Komunikasi: via email, wa. Keterlambatan tugas diakomodasi 1 minggu setelah pertemuan. Sistem Penilaian: 30 % tugas+presentasi, 35% UTS, 35% UAS. markovz@ccsu.edu Ingrid Russell University of Hartford irussell@hartford.edu Data Mining"Drowning in Data yet Starving for Knowledge" ???"Computers have promised us a fountain of wisdom but delivered markovzccsueduIngrid Russell University of HartfordirussellhartfordeduData MiningDrowning in Data yet Starving for Knowledge Computers have promised us a fountain of wisdom but delivered aflood of dat http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am.