Building Data Mining Applications for CRM Introduction This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections‚ each with a specific theme: • Classical Techniques: Statistics‚ Neighborhoods and Clustering • Next Generation Techniques: Trees‚ Networks and Rules Each section will describe a number of data mining algorithms at a high level‚ focusing on the "big picture" so that the reader will
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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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Generic Data Compression Techniques Data compression schemes fall into two categories. Some are lossless‚ others are lossy. Lossless schemes are those that do not lose information in the compression process. Lossy schemes are those that may lead to the loss of information. Lossy techniques provide more compression than lossless ones and are therefore popular in settings in which minor errors can be tolerated‚ as in the case of images and audio. In cases where the data being compressed consist
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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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Techniques for Summarizing Quantitative Data frequency histogram A sample of 40 female statistics students were asked how many times they cried in the previous month.Their replies were as follows: Stem-Leaf Plot A natural way to organize (group) quantitative data is with the order property of the real numbers‚ i.e.‚ arrange the data from least to greatest. For example‚ the 30 weights: 185‚ 160‚ 235‚ 165‚ 125‚ 175‚ 185‚ 132‚ 168‚ 112‚ 170‚ 155‚ 105‚ 158‚ 120‚ 190‚ 140
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| |Hand in Date |29/05/2013 | Assignment Introduction The assignment gives you the opportunity to develop techniques for data gathering and storage‚ an understanding of the tools available to create and present useful information‚ in order to make business decisions. Learning
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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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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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a decision support tool based on the Case Based Reasoning technique‚ meant to help physicians in the retrieval of past similar cases‚ able to provide a suggestion about the revision of diabetic patients’ therapy scheme. A case is defined as a set of features collected during a visit. A taxonomy of prototypical situations‚ or classes‚ has been formalized; a set of cases belonging to these classes has been stored into a relational data-base. For each input case‚ the system allows the physician
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