Data mining and predictive analysis intelligence gathering and crime analysis by Colleen McCue

Cover of: Data mining and predictive analysis | Colleen McCue

Published by Butterworth-Heinemann in Amsterdam, Boston .

Written in English

Read online

Subjects:

  • Crime analysis,
  • Data mining,
  • Law enforcement -- Data processing,
  • Criminal behavior, Prediction of

Edition Notes

Includes bibliographical references and index

Book details

StatementColleen McCue
Classifications
LC ClassificationsHV7936.C88 M37 2007
The Physical Object
Paginationxxxi, 332 p. :
Number of Pages332
ID Numbers
Open LibraryOL15599263M
ISBN 100750677961
ISBN 109780750677967
LC Control Number2006040568

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Data mining and predictive analytics can be used to operationalize Criminal Investigative Analysis, or the behavioral analysis of violent crime. Similar to the use of advanced analytics in other domains, behaviorally segmenting crime based on type, nature, and motive can provide novel, operationally relevant and actionable insight.

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In this book Dr. McCue. Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs.

Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that.

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Data Mining and Predictive Analytics Book Description: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.

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Colleen McCue pairs an educational background in neuroscience and psychology with extensive experience in the fields of behavioral science, cirme analysis, and intelligence gathering to create Data Mining and Predictive Analysis, a must-read for all law enforcement professionals.

Data Mining and Predictive Analytics, Second Edition: * Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language * Features over chapter exercises, allowing readers to assess their understanding of the new material * Provides a.

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Predictive Analytics, Data Mining and Big Data Myths, Misconceptions and Methods. Authors: Finlay, S. Free Preview. Broad, yet disciplined approach with extensive experience of private, academic, and government sectors the author is uniquely placed to offer valuable and relevant insights "If you want an excellent nontechnical overview of Brand: Palgrave Macmillan UK.

Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal dge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis.

The book is a starting /5(2). Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner r you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid Price: $ Data Mining Book Review: Predictive Analytics.

Published on J in data mining by Sandro Saitta. I had the pleasure to shortly meet Eric Siegel at a Predictive Analytics World conference in In addition to being the founder of PAW, Eric is also the author of the delightful book Predictive Analytics: the power to predict who will.

CHAPTER 1 AN INTRODUCTION TO DATA MINING AND PREDICTIVE ANALYTICS 3 What is Data Mining. What is Predictive Analytics. Wanted: Data Miners 5. The Need for Human Direction of Data Mining 6. The Cross-Industry Standard Process for Data Mining: CRISP-DM 6. CRISP-DM: The Six Phases 7.

Fallacies of Data Mining 9Brand: Wiley. Data Mining and Predictive Analytics book. Read reviews from world’s largest community for readers.

Learn methods of data analysis and their applicatio /5(6). In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions.

In this book Dr. McCue Brand: Elsevier Science. Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining) “This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.

Sholom M. Weiss and Nitin Indurkhya, Predictive Data Mining: A Practical Guide, Morgan Kaufmann, Graham Williams, Data Mining Desktop Survival Guide, on-line book (PDF). Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, ISBN   The book also explores predictive tasks, be them classification or regression.

Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems.

The learning resources offer. Data Mining and Predictive Analytics (DMPA) does the job very well by getting you into data mining learning mode with ease. This book has been a big help for me so far. Even though several key area of data mining is math and statistics dependent, this book helped me get into refresher mode and get going with my data mining s: Elsevier Science Release Date: Octo Imprint: Butterworth-Heinemann ISBN: Language: English Download options: EPUB 2 (Adobe DRM).

Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner r you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid.

Tips, tricks, and comments in data mining and predictive analytics, including data preprocessing, visualization, modeling, and model deployment.

Hosted by Dean Abbott, Abbott Analytics. Monday, Data evaluation (1) data mining (18) data mining books (4) data mining competition (1). Book Description. Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and.

Implementation files can be downloaded from the book companion site at Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a.

Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

About the Author Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining /5(39). Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions.

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals. Buy Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner by Kotu, Vijay, Deshpande, Bala (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders/5(23). In fact, methods and tools of data mining play an essential role in predictive analytics solutions; but predictive analytics goes beyond data mining.

For example, predictive analytics also uses text mining, on algorithms-based analysis method for unstructured contents such as articles, blogs, tweets, Facebook contents.”. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool.

"If learning-by-doing is your mantra -- as well it should be for predictive analytics -- this book will jumpstart your practice. Professor of Statistics and Data Science Founder, Data Mining @CCSU Central Connecticut State University Chantal Larose, PhD Asst Prof of Statistics and Data Science Eastern Connecticut State University Data Sets.

Data Sets. Da ta Sets. Data Sets. Adopter's Resources: Powerpoints. Solutions. Course Projects. Predictive Analytics: – Predictive analytics involves advanced statistical, modeling, data mining and one or more machine learning techniques to dig into data and allows analysts to make predictions.

Predictive analytics is used to forecast what will happen in future. Unlike Data mining, predictive analytics can be segregated in a three-step process- definition of the business goal, collecting additional data with the help of third-party tools, and drafting a predictive model that may involve of complex mathematical model or may be as simple as identifying the customers based on their geographic location or.

The main difference between data mining and predictive analytics is that the data mining is the process of identifying the hidden patterns of data using algorithms and mining tools while the predictive analytics is the process of applying business knowledge to the discovered patterns to make predictions.

Data Mining is the process of discovering the patterns in a large dataset.

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