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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will win is 60% and above.” Null Hypothesis “If X makes the first move then the probability of the player with X will win is less than 60%.” Data Collection and Preparation To prove or refute the hypothesis‚ data has to be collected. As we all know this step requires a great amount of time and effort. Also in order to build an effective model a data mining algorithm must be presented with a few hundred or few thousands relevant/applicable records. As mentioned above there are thousands of winning
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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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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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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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Why Data Mining can Aid HealthCare? Before talking about that why Data Mining can Aid Healthcare‚ there is a need to define that What is Data Mining‚ Why It is useful and Why it is required. What is Data Mining? Data Mining is the method of searching data from a large database. Data Mining is a technique that is used when we have a large database which is to be searched by applying some methods of searching. It is a technique that is applied when we have a concept of Data Warehouse. Data Warehouse
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Data Mining Project on IMDB website ABSTRACT The Internet Movie Database (IMDb) is an online database of information related to movies‚ television shows‚ stars‚ etc. We chose to do our project from 2008 to 2011 year’s movie database. We extracted data like Movie‚ Director‚ Star‚ Image Url‚ Studio from the IMDb website. For this extraction of data we used a tool named Mozenda. After the data extraction‚ the data was analyzed. For a particular star‚ his/her movie‚ director‚ studio with whom the
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Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values‚ outliers‚ incorrect values‚ duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for
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causes a Terrorist to be labeled a Revolutionist or a Revolutionist labeled a Terrorist. Today’s society usually uses the terms interchangeably; when one begins to talk of revolution‚ they are normally labeled as a terrorist. A terrorist is normally labeled a terrorist by their enemy‚ but a hero by the people they fight for. Is it possible that a person who has been labeled as a Terrorist to actually have good intentions for those he fights for? Is it possible they are labeled a Terrorist only due
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