2.1 Assuming that data mining techniques are to be used in the following cases‚ identify whether the task required is supervised or unsupervised learning. a. Supervised-Deciding whether to issue a loan to an applicant based on demographic and financial data (with reference to a database of similar data on prior customers). b. Unsupervised-In an online bookstore‚ making recommendations to customers concerning additional items to buy based on the buying patterns in prior transactions. c. Supervised-Identifying
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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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WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining
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Annotated Bibliography Data Mining Ayanso‚ A.‚ & Yoogalingam‚ R. (2010). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce‚ 14(1)‚ 79-113. doi:10.2753/JEC1086-4415140103 This article introduces the utilization of cluster analysis as a data mining tool. E-commerce has forced traditional businesses to reform their decision making processes and conduct its affairs based on activities occurring online. Monitoring
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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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contains only three base cells: (1) (a1‚ b2‚ c3‚ d4; ...‚ d9‚ d10)‚ (2) (a1‚ c2‚ b3‚ d4‚ ...‚ d9‚ d10)‚ and (3) (b1‚ c2‚ b3‚ d4‚ ...‚ d9‚ d10)‚ where a_i != b_i‚ b_i != c_i‚ etc. The measure of the cube is count. 1‚ How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2‚ How many nonempty aggregate (i.e.‚ non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27
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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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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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billion bytes of data in digital form be it on social media‚ blogs‚ purchase transaction record‚ purchasing pattern of middle class families‚ amount of waste generated in a city‚ no. of road accidents on a particular highways‚ data generated by meteorological department etc. This huge size of data generated is known as big data. Generally managers use data to arrive at decision. Marketers use data analytics to determine customer preferences and their purchasing pattern. Big data has tremendous potential
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More than Data Warehouse- An insight to Customer Information Ritu Aggrawal – agg_ritu@rediffmail.com Deepshikha Kalra -deepshikha_ishan@yahoo.co.in working with MERI affiliated to GGSIPU‚ Delhi ABSTRACT The business requirements of an enterprise are constantly changing and the changes are coming at an exponential rate. Like advances in Information Technology have helped companies to quickly match competition. As a result‚ product quality and cost are no longer significant competitive
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