Mining Changes for Real-Life Applications Bing Liu‚ Wynne Hsu‚ Heng-Siew Han and Yiyuan Xia School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 {liub‚ whsu‚ xiayy}@comp.nus.edu.sg Abstract. Much of the data mining research has been focused on devising techniques to build accurate models and to discover rules from databases. Relatively little attention has been paid to mining changes in databases collected over time. For businesses‚ knowing what is changing and
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Glencore‚ Xstrata and the Restructuring of the Global Copper Mining Industry in 2012 Diana Alvarez Valencia (1310200) University Canada West Dr. Paul Rome MGMT 661 Strategic Management Tuesday‚ May 12‚ 2015 Introduction and Problem Identification In this case study we will identify the problems that can be issued in the process of the merger between two of the largest commodities traders in the world‚ Glencore and Xstrata. It will provide the background of both companies‚ the situation analysis
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ABSTRACT This study attempted to estimate the environmental impact of Foreign Direct Investment in the mining sector in Nigeria. It is argued that only those countries that have reached a certain income level can absorb new technologies and benefit from technology diffusion‚ and thus reap the extra advantages that FDI can offer. The mining industry in Nigeria is dominated by oil. Indeed‚ Nigeria is the largest producer of this commodity in Africa and sixth largest producers in the world. This research
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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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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 commonly referred
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Use of Data Mining in Fraud Detection Focus on ACL Hofstra University Abstract This paper explore how business data mining software are used in fraud detection. In the paper‚ we discuss the fraud‚ fraud types and cost of fraud. In order to reduce the cost of fraud‚ companies can use data mining to detect the fraud. There are two methods: focus on all transaction data and focus on particular risks. There are several data mining software on the market‚ we introduce seven
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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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A data mining approach to analysis and prediction of movie ratings M. Saraee‚ S. White & J. Eccleston University of Salford‚ England Abstract This paper details our analysis of the Internet Movie Database (IMDb)‚ a free‚ user-maintained‚ online resource of production details for over 390‚000 movies‚ television series and video games‚ which contains information such as title‚ genre‚ box-office taking‚ cast credits and user ’s ratings. We gather a series of interesting facts and relationships
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Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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is a passion. COAL COAL MINING CONTRACTORS Our Coal Consultants have in-depth knowledge of the coal industry and have been active participants in coal mining‚ coal preparation and handling‚ quality control and equipment procurement FABSI INTERNATIONAL & MMI 92 323 5146578 http://pakistancoal.blogspot.com/ Experience FASBI INTERNATIONAL & MMI Our Coal Consultants have in-depth knowledge of the coal industry and have been active participants in coal mining‚ coal preparation and handling
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