PPT-DATA MINING LECTURE 11 Classification

Basic Concepts Decision Trees Evaluation NearestNeighbor 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. CS548 Xiufeng . Chen. S. ources. K. . Chitra. , . B.Subashini. , Customer Retention in Banking Sector using . Predictive . Data . Mining Technique. , International Conference on . Information . Technology, . 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. Ryan . S.J.d. . Baker. PSLC Summer School 2010. Welcome to the EDM track!. Educational Data Mining. “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” . 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.). 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. Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Lesson 1. Bernhard Pfahringer. University of Waikato, New Zealand. 2. Or:. Why . YOU. should care about Stream Mining. Overview. 3. Why is stream mining important?. How is it different from batch ML?. Ryan . S.J.d. . Baker. PSLC Summer School 2012. Welcome to the EDM track!. On behalf of the track lead, John Stamper, and all of our colleagues. Educational Data Mining. “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” . Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. English 1102:. Shakespeare. Fall, 2014. The History of Henry V. William . Shakespeare. Data Mining . Shakespeare. The Tragedy of Hamlet. William Shakespeare. Data Mining . Shakespeare. Twelfth Night, Or What You Will. 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.. with an . Eclipse . Attack. With . Srijan. Kumar, Andrew Miller and Elaine Shi. 1. Kartik . Nayak. 2. Alice. Bob. Charlie. Emily. Blockchain. Bitcoin Mining. Dave. Fairness: If Alice has 1/4. th. computation power, she gets 1/4. 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.