Commentary On: Bauxite Mining Issue in Orissa. (Draft #1; I’ve just done my research and written up‚ i still have to put it all together‚ produce graphs and more) Article link : http://www.guardian.co.uk/business/2010/aug/24/vedanta-mine-plan-halted-indian-government In a conference or board meeting room‚ business leaders should get down to the nitty-gritty interest‚ such as minimizing social cost instead of minimizing private costs. Often long-term environmental damage is overlooked by short-term
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------------------------------------------------- Tzu Han Hung (Vivian) CASE 2 1. Estimated profit by random selection Expected spending per catalog mailed = 0.053 * $103 = $5.46 Expected Gross Profit by random select= (5.46-2)*180‚000 = $622‚800 2. a) We applied partition to “All_data” sheet‚ and partition output is shown in “Data_Partition1” b) Logistic regression output can be seen in “LR_Output1”. Target variable is “purchase”. We select every variable except sequence_number(meaningless
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the business once the company develops connectedness and meaning‚ transforming it into information‚ knowledge‚ and eventually wisdom. As described in the case‚ the following are the benefits derived by the businesses: • Applebee’s utilized data mining technology to analyze both the front-of-house and back-of-house performances. They also used the stored data for effective inventory management (supplies replenishment) and identifying which products to promote. • Travelocity‚ an online travel site
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Recommended Systems using Collaborative Filtering and Classification Algorithms in Data Mining Dhwani Shah 2008A7PS097G Mentor – Mrs. Shubhangi Gawali BITSC331 2011 1 BITS – Pilani‚ K.K Birla Goa INDEX S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. Topic Introduction to Recommended Systems Problem Statement Apriori Algorithm Pseudo Code Apriori algorithm Example Classification Classification Techniques k-NN algorithm Determine a good value of k References Page No. 3 5 5 7 14 16 19 24 26 2
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BSBHRM501B- Manage Human Resources Services. Morobe Mining Joint Ventures (PNG) Human Resources Services. Chosen Area: Career management Services: Dealing with complaints Implementing new legislation and laws Mentoring and coaching WH&S ‚ pay role i.e. leave entitlement Recruitment/ advertising Training for possible advancement Strategic and Operational Plans: Valuing our employees: By making all employees
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Arctic Mining Case Study Tom Parker‚ 43‚ is now a field technician and coordinator for Arctic Mining Consultants. In the past he’s held various positions in non-technical aspects of mineral exploration. His past experiences include claim staking‚ line cutting‚ grid installation‚ soil sampling‚ prospecting‚ and trenching. For this project Parker will be acting as project manger though this is not his normal role. His responsibilities include hiring‚ training‚ and supervising a team of field assistants
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Introduction to Data Mining Summer‚ 2012 Homework 3 Due Monday June.11‚ 11:59pm May 22‚ 2012 In homework 3‚ you are asked to compare four methods on three different data sets. The four methods are: • Indicator Response Matrix Linear Regression to the Indicator Response Matrix. You need to implement the ridge regression and tune the regularization parameter. The material of this algorithm can be found in Page 103 to Page 106 in the book ”The Elements of Statistical Learning” (http://www-stat
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Data Warehouses and Data Marts: A Dynamic View file:///E|/FrontPage Webs/Content/EISWEB/DWDMDV.html Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone‚ Ph.D. White Paper No. Three March 27‚ 1997 Patterns of Data Mart Development In the beginning‚ there were only the islands of information: the operational data stores and legacy systems that needed enterprise-wide integration; and the data warehouse: the solution to the problem of integration of diverse and often redundant
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// FREQUENT SUBTREE MINING ALGORITHM... #include #include #include #include #include #include using namespace std; FILE *fp; int no_of_nodes=0‚ string_ctr=0‚ vect_ctr=0‚ vect_ctr1=0‚pos_ctr=0‚*pos; struct MyNode { string name; vector children; }*myroot‚ *myroot1‚ **tree_pattern‚ **subtree_pattern; //FUNCTION PROTOTYPES DECLARATION ... static void print_element_names(xmlNode *); static MyNode* preprocess(xmlNode *‚MyNode *‚ int); int printMyNode(MyNode *); void
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model and try apply the class survived or didn’t survive. If I apply a decision tree to the dataset as it is‚ I get a prediction rate of 78%. I will try various techniques throughout this report to increase the overall prediction rate. Data mining objectives: I would like to explore the pre conceived ideas I have about the sinking of the titanic‚ and prove if they are correct. Was there a majority of 3rd class passengers who died? What was the ratio of passengers who died‚ male or female
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