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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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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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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Web Usage Mining: A Survey on Pattern Extraction from Web Logs Web Usage Mining: A Survey on Pattern Extraction from Web Logs S. K. Pani‚ ‚ 2L. Panigrahy‚ 2V.H.Sankar‚ 3Bikram Keshari Ratha‚ 2A.K.Mandal‚ 2S.K.Padhi 1 P.G. Department Of Computer Science‚ RCMA; Bhubaneswar‚ Orissa‚ India 2 Department of Computer Science and Engineering; Konark Institute of Science and Technology; Bhubaneswar‚ Orissa‚ India 3 P.G. Department Of Computer Science‚ Utkal University‚Bhubaneswar‚ Orissa‚ India E-mail:
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A data mining approach to analysis and prediction of movie ratings M. Saraee‚ S. White & J. Eccleston University of Salford‚ England Abstract This paper details our analysis of the Internet Movie Database (IMDb)‚ a free‚ user-maintained‚ online resource of production details for over 390‚000 movies‚ television series and video games‚ which contains information such as title‚ genre‚ box-office taking‚ cast credits and user ’s ratings. We gather a series of interesting facts and relationships
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Data Warehouses and Data Marts: A Dynamic View file:///E|/FrontPage Webs/Content/EISWEB/DWDMDV.html Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone‚ Ph.D. White Paper No. Three March 27‚ 1997 Patterns of Data Mart Development In the beginning‚ there were only the islands of information: the operational data stores and legacy systems that needed enterprise-wide integration; and the data warehouse: the solution to the problem of integration of diverse and often redundant
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Assignment : Data Mining Student : Mohamed Kamara Professor : Dr. Albert Chima Dominic Course : CIS 500- Information Systems for Decision Making Data : 06/11/2014 This report is an analysis of the benefits of data mining to business practices
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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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Case study: Jaeger uses data mining to reduce losses from crime and waste Leg of lamb is the most stolen item at Iceland. Thieves also like cheese‚ bacon and coffee. With the UK in recession‚ shoplifters appear to be switching their sights from alcohol‚ electric toothbrushes and perfume to food. Tesco‚ Marks & Spencer and Iceland have all reported an increase in shoplifting since the economy began to contract in the second quarter of 2008. Tesco alone caught some 43‚000 would-be thieves in the first
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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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