Learning and Data Mining Overview: Efficient asset allocation through statistical learning methods and comparison of methods for the creation of an index tracking ETF (Exchange traded fund) Datasets: The datasets are chosen from the website of the book “Statistics and Data Analysis for Financial Engineering” by David Ruppert. The book is mentioned as one of the references for this course. The two data sets chosen are 1. Stock_FX_Bond.csv 2. Stock_FX_Bond_2004_to_2006.csv The data includes
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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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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan‚ Steinbach‚ Kumar © Tan‚Steinbach‚ Kumar Introduction to Data Mining 4/18/2004 1 Why Mine Data? Commercial Viewpoint O Lots of data is being collected and warehoused – Web data‚ e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions O Computers have become cheaper and more powerful O Competitive Pressure is Strong – Provide better‚ customized services for an edge (e.g
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manage large volumes of business data. The use of database systems in supporting applications that employ query based report generation continues to be the main traditional use of this technology. However‚ the size and volume of data being managed raises new and interesting issues. Can we utilize methods wherein the data can 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
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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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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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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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regression model to testing and validation dataset (output is in “LR_Output2”‚ “LR_Testscore2”‚ and “LR_ValidLiftChart2”). In testcore sheet‚ we can see the probability output we generated for each row from test data. Below shows the regression model and scoring summary. 3. a) the data of purchaser only is in “Purchasers_only” sheet b) Partition is shown in “Data_Partition2” sheet c) Multiple Linear regression output can be seen in “MLR_Output1”. Target variable is “spending”. We select every
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to create and operate data warehouses such as those described in the case? Do you see any disadvantages? Is there any reason that all companies shouldn’t use data warehousing technology? Information is the most important tool when making business decisions. As O’Brien and Marakas stated‚ “Today’s business enterprises cannot survive or succeed without quality data about their internal operations and external environment.” Companies that have large amounts of available data can use the information
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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? Did the location of cabins make a difference as to who survived? Did chivalry ring through and did ‘women and children first’ actually happen? Data Understanding: Describe the data: Figure Class
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