International Journal For Technological Research In Engineering Volume 1, Issue 9, May-2014 ISSN (Online): 2347 - 4718 DATA MINING TECHNIQUES TO ANALYZE CRIME DATA R. G. Uthra, M. Tech (CS) Bharathidasan University, Trichy, India. Abstract: In data mining, Crime management is an interesting application where it plays an important role in handling of crime data. Crime investigation has very significant role of police system in any country. There had been an enormous increase in the crime
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Analytics Using Data Mining Submitted by: (Student names) Group Members (8A) Harneet Chawla Ankit Sobti Kanika Miglani Varghese Cherian Saad Khan Note: Considering our client is an FMCG, each technique mentioned below has been explained in detail ensuring thorough/easy understanding. Business Analytics Using Data Mining – Final Project Valentine Coupon Scheme Executive summary Business problem We have been hired by our client, a reputed FMCG conglomerate, Unilever as data mining consultants
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Trends in Information Analysis and Data Management Trends in Information Analysis and Data Management Over the last decade, advancements in digital technology have enabled companies to collect huge amounts of new information. This data is so large in scope, it has traditionally been difficult to process and analyze this information using standard database management systems such as SQL. The commoditization of computer technology has created a new paradigm in which data can be analyzed more efficiently
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Data Mining Third Edition This page intentionally left blank Data Mining Practical Machine Learning Tools and Techniques Third Edition Ian H. Witten Eibe Frank Mark A. Hall AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann Publishers is an imprint of Elsevier Morgan Kaufmann Publishers is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA This book is printed
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MSc. Information System Management Kyaw Khine Soe (3026039) Data Mining and Business Analytics Boston Housing Dataset Analysis. Table of Contents Introduction 3 Problem Statement 3 The associated data of Boston 5 Data pre-processing / Data preparation 8 Clustering Analysis 11 Cluster segment profile 17 Regression Analysis 18 Predictive analysis using neural network node 19 Decision tree node 21 Regression node analysis 23 Model Comparison 24 The recommendation and conclusion
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Introduction Clustering in data mining, is useful in discovery of distribution patterns in underlying data. Our interest is in clustering based on non-numerical data-categorical or Boolean attributes. An example of hierarchical clustering algorithm used in sample data is ROCK (RObust Clustering using linKs). The clustering technique is useful for grouping data points such that a single group or cluster have similar characteristics while different groups are dissimilar. ROCK belongs to the class
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com/pubs or call 1-800-727-3228. SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Contents Chapter 1 Data Mining Overview 1 Layout of the Enterprise Miner Window 2 Organization and Uses of Enterprise Miner Nodes 7
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De-Identified Personal Health Care System Using Hadoop The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this BigData and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination
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As discussed, we will be doing some data processing, model building, and evaluation using the data science/machine learning toolkit Weka. Your first task will be simply to get Weka installed and to run through building a model, to make sure Weka is working fine on your computer. As your data mining task, you will build a classification tree model (see Chapter 3) from the mailing data set described in the Python tutorial. We will talk about this later in class, but to run Weka through its paces
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Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm
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