PPT-Data Mining Association Analysis: Basic Concepts

Author : calandra-battersby | Published Date : 2018-02-28

and Algorithms From Introduction to Data Mining By Tan Steinbach Kumar Association Rule Mining Given a set of transactions find rules that will predict the occurrence

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Data Mining Association Analysis: Basic Concepts: Transcript

and Algorithms From Introduction to Data Mining By Tan Steinbach Kumar Association Rule Mining Given a set of transactions find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction. Daniel Johnston and . Nabeel. . Hanif. Aim. To look at the use of data mining within the . Television and Film. industry.. To . examine how . DM is able to improve . the . Tv. /Film . industry for both viewers and companies. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” . Prepared by: Eng. . Hiba. Ramadan. Supervised by: . Dr. . Rakan. . Razouk. . Outline. Introduction. key directions in the field of privacy-preserving data mining. Privacy-Preserving Data Publishing. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Presentation by:. ABHISHEK KAMAT. ABHISHEK MADHUSUDHAN. SUYAMEENDRA WADKI. 1. Introduction. Mining the data to find interesting patterns, useful insights, customer data and their relationship - data mining . 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. Chapter 6. . Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , . 2017. 1. Chapter 6: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. Professor Tom . Fomby. Director. Richard B. Johnson Center for Economic Studies. Department of Economics. SMU. May 23, 2013. Big Data:. Many Observations on Many Variables . Data File. OBS No.. Target Var.. 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. Core Methods in Educational Data Mining EDUC 691 Spring 2019 Assignment BA4 Questions? Comments? Concerns? Association Rule Mining Today’s Class The Land of Inconsistent Terminology Association Rule Mining Prepared by David Douglas, University of Arkansas. Hosted by the University of Arkansas. 1. IBM SPSS . Association Analysis. Also referred to as. Affinity Analysis. Market Basket Analysis. For MBA, basically means what is being purchased together. Credit: Gaby . Matalon. What is Data Mining?. The. . process . of analyzing data from different perspectives and summarizing it into useful information. It . uncovers patterns . in a large set of data. Course webpage:. http://www.cs.bu.edu/~. evimaria/cs565-11.html. Schedule: Mon – Wed, . 2:30-4:00. Instructor: . Evimaria. . Terzi. , . evimaria@cs.bu.edu. Office hours: . Tues. . 11. :00am-12:30pm.

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