The Goal My goal is to find the probability of wining in tic-tac-toe game given that you make the first move. To obtain hypothesis bases on my goal I have to state some conditions and facts on the game. They are: 1) There are 362‚ 880 ways of placing O’s and X’s. 2) When X make first move‚ possibility of X winning is 131‚184‚ O winning is 77‚ 904‚ and 46‚ 080 tied games (Source: http://en.wikipedia.org/wiki/Tic-tac-toe). After eliminating rotations and/or reflections of other outcomes‚ there are
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Building Data Mining Applications for CRM Introduction This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections‚ each with a specific theme: • Classical Techniques: Statistics‚ Neighborhoods and Clustering • Next Generation Techniques: Trees‚ Networks and Rules Each section will describe a number of data mining algorithms at a high level‚ focusing on the "big picture" so that the reader will
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multidimensional set of data. Henceforth‚ by applying Data Mining (DM) algorithms for Business Intelligence‚ it is possible to automate the analysis process‚ thus comes the ability to extract patterns and other important information from the data set. Understanding the reason why Data Mining is needed in Business Intelligence and also the process‚ applications and different tasks that Data Mining provides for Business Intelligence purposes is the main subject area in this essay. Data mining process is also
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R and Data Mining: Examples and Case Studies 1 Yanchang Zhao yanchang@rdatamining.com http://www.RDataMining.com April 26‚ 2013 1 ➞2012-2013 Yanchang Zhao. Published by Elsevier in December 2012. All rights reserved. Messages from the Author Case studies: The case studies are not included in this oneline version. They are reserved exclusively for a book version. Latest version: The latest online version is available at http://www.rdatamining.com. See the website also for an R Reference Card
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Department of Computer Science Database and Data Mining‚ COS 514 Dr. Chi Shen Homework No. 8‚ Chapter 13‚ Aklilu Shiketa Q13. 3 Cosmetic Purchases Consider the following Data on Cosmetics Purchases in Binary Matrix Form a) Select several values in the matrix and explain their meaning. Value Cell Meaning 0 For example‚ Row 1‚ Column2 At transaction #1 bag was not purchased. (shows absence of Bag in the transaction) 1 Row 10‚ column (2 and 3) “If a Bag is purchased‚ a Blush is also purchased
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Data Mining: A Tool for the Enhancement of Banking Sector Shipra Kalra; Rachika Gupta kalra.shipra87@gmail.com; guptarachika@yahoo.co.in Lecturer‚ Chanderprabhu Jain College of Higher Studies and School of Law‚ Sector A-8‚ Narela‚ Delhi-110040 Abstract Data mining is emerging as a very useful tool for providing valuable information from large databases and enabling managers and business executives to make hard core decisions in a much easier and effective manner. It is a process of analyzing the
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DATA MINING FOR POTENTIAL CUSTOMERS: East – West Airlines/Telcon Jermaine Paul 12/12/2013 BUSINESS PROBLEM East-West Airlines (EA) is entering into partnership with the cellular service provider‚ Telcon‚ by marketing their service through direct mail. In order to achieve this‚ EA dataset is provided to categorize their customers to identify which ones would be likely to purchase Telcon’s services through direct mail. If the accurate categorization is done the partnership will save
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Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values‚ outliers‚ incorrect values‚ duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for
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Measurement in Physical Education and Exercise Science‚ 17: 171–186‚ 2013 Copyright © Taylor & Francis Group‚ LLC ISSN: 1091-367X print / 1532-7841 online DOI: 10.1080/1091367X.2013.805137 Data Mining in Elite Sports: A Review and a Framework Bahadorreza Ofoghi Institute of Sport‚ Exercise‚ and Active Living‚ Victoria University Melbourne‚ Victoria‚ Australia Victorian Institute of Sport Albert Park‚ Victoria‚ Australia John Zeleznikow and Clare MacMahon Institute of Sport‚ Exercise
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Data Mining Project on IMDB website ABSTRACT The Internet Movie Database (IMDb) is an online database of information related to movies‚ television shows‚ stars‚ etc. We chose to do our project from 2008 to 2011 year’s movie database. We extracted data like Movie‚ Director‚ Star‚ Image Url‚ Studio from the IMDb website. For this extraction of data we used a tool named Mozenda. After the data extraction‚ the data was analyzed. For a particular star‚ his/her movie‚ director‚ studio with whom the
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