3. DATA MINING TECHNIQUES 3.1 NECESSITY OF DATA MINIING DATA Data is numbers or text which is a statement of a fact. It is unprocessed and stored in database for further analysis. Operational and transaction data such as cost and sales‚ is essential to modern enterprise’s internal environment. Non-operational data such as competitors’ sales and forecasting data‚ is responsible for analysis of external environment. INFORMATION Information is generated through data mining so that it becomes
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Systems The goal of the term project is to develop a useful and viable prediction or classification model based on data. You will need to develop a research question‚ which you refine further based on the availability of data. You may need to merge multiple data sets together. Process: • Each team of 2 or 3 students will work on a business problem involving data analysis with real data. The project will focus on classification and prediction methods we covered during the semester. • A presentation
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Excellence for Data Mining in Egypt By: Aref Rashad I- Introduction The convergence of computer resources connected via a global network has created an information tool of unprecedented power‚ a tool in its infancy. The global network is awash with data‚ uncoordinated‚ unexplored‚ but potentially containing information and knowledge of immense economic and technical significance. It is the role of data mining technologies arising from many discipline areas to convert that data into information
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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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The differences between data and information: a short analysis What is data? I have researched on the Internet and in books for the meaning and description of data and there are a variety of meanings and some are very misleading the best two meanings I found were “data consists of random (or a set of random) unprocessed facts with little or no intrinsic value”(S. Yull‚ T. Stump p-6) and “Data: the raw facts and figures a computer accepts as input and then processes to produce useful information”
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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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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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