help businesses achieve competitive advantage‚ can the data be used to model underlying business processes‚ and can we gain insights from the data to help improve business processes? These are the goals of Business Intelligence (BI) systems‚ and Data Mining is the set of embeddable (in BI systems) analytic methods that provide the capabilities to explore‚ summarize‚ and model the data. Before applying these methods to data‚ the data has to be typically organized into history repositories‚ known as data
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Mining Speech Good morning/ afternoon fellow classmates and teacher‚ the topic of my speech is one of Canada’s Industries- mining. Mining is a big part of Canada’s economy and is a major contributor‚ contributing to nearly 5% of Canada’s Gross Domestic Product (also known as GDP). The exporting of aluminum‚ copper‚ gold‚ iron and steel‚ iron ore‚ nickle‚ silver‚ uranium‚ zinc‚ diamonds‚ potash‚ and coal ranged from $1.7 billion to $15.1 billion. As we can see from the point just said‚ mining
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In this case study‚ we see how Tom Parker managed his team for the company Arctic Mining Consultants. Parker his responsable to hiring‚ training and supervising a team of assistants fields. A project manager made a report to Parker concerning the result of the objective. The team are composed by 3 fields assistants : John Talbot‚ Greg Boyce and Brian Miller all of them had work before with Parker. The team need to stake a fifteen claims in Eagle lake‚ British Colombia in Canada : marks a line with
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Data Warehousing and Data mining December‚ 9 2013 Data Mining and Data Warehousing Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any
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Bullock Gold Mining Seth Bullock‚ the owner of Bullock Gold Mining‚ is evaluating a new gold mine in South Dakota. Dan Dority‚ the company’s geologist‚ has just finished his analysis of the mine site. He has estimated that the mine would be productive for eight years‚ after which the gold would be completely mined. Dan had taken an estimate of the gold deposits to Alma Garrett‚ the company’s financial officer. Alma has also been asked by Seth to perform an analysis of the new mine and present
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A Paper on Data preprocessing and Measures of Similarities and Dissimilarities and Data Mining Applications DEEPAK KUMAR D R M.SC IN COMPUTER SCIENCE 3RD SEMESTER‚ DAVANGERE UNIVERSITY deepakrdevang@gmail.com Abstract: This topic is mainly used by a number of data mining techniques‚ such as clustering‚ nearest neighbor classification‚ and anomaly detection. And it can also include the data mining applications.In this paper we have focused a variety of techniques‚ approaches and different areas
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Data Mining and Actionable Information May 24‚ 2014 Data Mining and Actionable Information People need information for planning their work‚ meet deadlines‚ and achieve their goals. They also need information to analyze problems and make important decisions. Data is most definitely not in short supply these days‚ but not all data is useful or reliable. Actionable information offers data that can be used to make effective and specific business decisions (Soatto‚ 2009). In order
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EXECUTIVE SUMMARY This study takes an insight into the usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are: a) Data warehousing b) Data Mining c) ETL d) Data Mart An attempt has been made to analyse different ways of using these for the enhancement in the
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Hedging in the Mining Industry Strategy‚ Control and Governance Contents Foreword Chapter 1: Executive summary Chapter 2: To hedge or not to hedge? Considering a strategy 1 2–4 5 – 12 Chapter 3: What tools are available? Implementing the hedging strategy 13 – 24 Chapter 4: How do we control and monitor a hedging programme? 25 – 36 Chapter 5: How‚ why and to whom do we communicate our risk-management strategy? 37 – 44 Chapter 6: What are the accounting implications? 45 – 49 Appendix
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Title: “Data Mining: The Mushroom Database” Author: Hemendra Pal Singh* In this review “Data Mining: The Mushroom Database” is focuses in the study of database or datasets of a mushroom. The purpose of the research is to broaden the preceding researches by administer new data sets of stylometry‚ keystroke capture‚ and mouse movement data through Weka. Weka stands for Waikato environment for knowledge analysis‚ and it is a popular suite of machine learning software written in Java‚ developed at
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