...Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. Sistemas de Informação Ricardo Campos (ricardo.campos@ipt.pt) © Ricardo Campos [ h t t p : / / w w w . c c c . i p t . p t / ~ r i c a r d o ] Sistemas de Informação Autoria Esta apresentação foi desenvolvida por Ricardo Campos, docente do Instituto Politécnico de Tomar. Encontra-se disponível na página web do autor no link Publications ao abrigo da seguinte licença: Mais detalhes em: http://creativecommons.org/licenses/by-nc/3.0/deed.pt O seu uso, de parte ou da totalidade, pressupõe a utilização da seguinte referência: Campos, Ricardo. (2008). Apresentação de Sistemas de Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. A sua disponibilização em formato PPT pode ser feita mediante solicitação (email: ricardo.campos@ipt.pt) © Ricardo Campos [ h t t p : / / w w w . c c c . i p t . p t / ~ r i c a r d o ] Sistemas de Informação Ricardo Campos [http://www.ccc.ipt.pt/~ricardo/] 1 Campos, Ricardo. (2008). Apresentação de Sistemas de Informação. Data Warehouse, SQL Server Business Intelligence Development Studio. Conceitos de CRM e Data Mining. Tabelas Dinâmicas no MS Excel. 417 slides. Bibliografia Recursos: Ralph Kimball, Laura Reeves, Margy Ross, Warren Thornthwaite The Data Warehouse Lifecycle...
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...INTRODUCTION TO BUSINESS ANALYTICS Sumeet Gupta Associate Professor Indian Institute of Management Raipur Outline • Business Analytics and its Applications • Analytics using Data Mining Techniques • Working with R BUSINESS ANALYTICS AND ITS APPLICATIONS What is Business Analytics? Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Evolution of Business Analytics? • Operations research • Management science • Business intelligence • Decision support systems • Personal computer software Application Areas of Business Analytics • Management of customer relationships • Financial and marketing activities • Supply chain management • Human resource planning • Pricing decisions • Sport team game strategies Why Business Analytics? • There is a strong relationship of BA with: • profitability of businesses • revenue of businesses • shareholder return • BA enhances understanding of data • BA is vital for businesses to remain competitive • BA enables creation of informative reports Global Warming Poll Winner Sales Revenue Predicting Customer Churn Credit Card Fraud Loan Default Prediction Managing Employee Retention Market Segmentation Medical Imaging Analyzing Tweets stylus ...
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...Rexer Analytics 4th Annual Data Miner Survey – 2010 Survey Summary Report – For more information contact Karl Rexer, PhD krexer@RexerAnalytics.com www.RexerAnalytics.com Outline • Overview & Key Findings • Where & How Data Miners Work • What’s Important to Data Miners • Data Mining Tools: Usage & Satisfaction • Overcoming Challenges & Optimism about the Future • Appendix: Where do Data Miners Come From? • Appendix: Rexer Analytics © 2011 Rexer Analytics 2 Overview & Key Findings © 2011 Rexer Analytics 3 2010 Data Miner Survey: Overview Vendors Corporate • Fourth annual survey NGO / Gov’t • 50 questions • Data collected online in early 2010 Academics Consultants • 10,000+ invitations emailed, plus promoted by newsgroups, vendors, and bloggers • Respondents: 735 data miners from 60 countries Note: Data from tool vendors (companies making data mining software) was excluded from many analyses. © 2011 Rexer Analytics Central & South America (4%) • Columbia 2% • Brazil 1% Asia Pacific • India 4% • Australia 3% • China 2% Middle East & Africa (3%) • Israel 1% • Turkey 1% North America • USA 40% • Canada 4% Europe • Germany 7% • UK 5% • France 4% • Poland 4% 4 Key Findings • FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past four years. Fittingly, “improving the understanding of customers”...
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...THE IMPACT OF DATA MINING ON MARKET PLACE Abstract Knowledge discovery and data mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. This article has shed light on the various market places that arises due to the data mining function. Data mining is concerned with the secondary analysis of large market place in order to find previously unknown relationships which are of importance to the organization owners. New problem arises, is that brand works according to the customer demand or not so the organization conduct research to know what the customer want and which way through satisfy the customer. Data mining enable us to discover the hidden pattern of knowledge in market place that is previously unknown. These discoveries of new channels help the organization work according to the customer demand and segmented the customers to maintain the share in the market. In this research also discuss the social network that organization used to advertised there brand in the market to aware the customers about the brand. In this research the data mining relationship with market place is found to be positively and semi strong. The objective of the research were to: create a general awareness about the impact of data mining on consumer demand and social network, to identify the market place those that are affected by data mining and to take preventive measures to prevent these consumer demand and social...
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...TWITTER ANALYSIS (IN RSTUDIO USING R PROGRAMMING LANGUAGE) Prepared By: KAIFY RAIS in.linkedin.com/pub/kaify-rais/31/346/886/ Acknowledgement This project is done as a final project as a part of the training course titled “Business Analytics with R”. I am really thankful to our course instructor Mr. Ajay Ohri, Founder, DecisionStats, for giving me an opportunity to work on the project “Twitter Analysis using R” and providing me with the necessary support and guidance which made me complete the project on time. I am extremely grateful to him for providing me the necessary links and material to start the project and understand the concept of Twitter Analysis using R. In this project “Twitter Analysis using R” , I have performed the Sentiment Analysis and Text Mining techniques on “#Kejriwal “. This project is done in RStudio which uses the libraries of R programming languages. I am really grateful to the resourceful articles and websites of R-project which helped me in understanding the tool as well as the topic. Also, I would like to extend my sincere regards to the support team of Edureka for their constant and timely support. Table of Contents Introduction 4 Limitations 4 Tools and Packages used 5 Twitter Analysis: 6 Creating a Twitter Application 6 Working on RStudio- Building the corpus 8 Saving Tweets 11 Sentiment Function 12 Scoring tweets and adding column 13 Import the csv file 14 Visualizing the tweets 15 Analysis & Conclusion...
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...Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? Mieke Jans, Nadine Lybaert, Koen Vanhoof Abstract Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study. 1 Introduction Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim...
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...Data Mining Data mining began with the advent of databases. Databases are warehouses full of computer data. Computer scientists began to realize that this data contains patterns and relationship to other sets of data. As computer technology emerged, data was extracted into useful information. Often, hidden relationships began to appear. Once this data became known and useful, industries grew around data mining. Data mining is a million dollar business aimed at improving marketing, research, criminal apprehension, fraud detection and other applications. History of Data Mining Computers began to be more widely used in the 1960’s. Computers were used to collect and store data. The data was stored on tapes and disks. The companies and organizations began to wonder about the data that was stored. They wanted to know about past sales, past performances and other pertinent information that was stored on these tapes and disks. The next step was to find an accurate way to retrieve the needed information without manually reading all the data. The next step in this quest came in the 1980’s with relational databases and structured queries. Query language could be used to find out more of what was in the data. The companies and organizations could now identify what has happened in the past. They also wanted to know how to apply this knowledge to future predictions based on past performances. In 1989, the first knowledge discovery workshop was held in Detroit (SQL Data Mining, 2012)...
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...without the written permission of the publisher. ISBN 978-1-921768-44-6 2 Ai Group National CEO Survey 2013 Business prospects in 2013 Australian Industry Group National CEO Survey Business prospects in 2013: Australia's gap year? Ai Group National CEO Survey 2013 Business prospects in 2013 3 Key messages Business prospects in 2013: Australia's gap year? The Australian economy is going through significant change, with multiple, long-term forces restructuring our economy (such as global growth shifts and our own demographic changes), and ongoing challenges in our immediate outlook (such as the high Australian dollar and our relatively high business cost base). Recent drivers of growth are waning, with capital investment by the mining industry due to peak soon and federal and state government investment already past its post-GFC peak. But other potential growth drivers – most notably commercial and residential investment – are yet to show signs of a meaningful pickup. The global outlook also remains challenging, with only Asia generating any real growth in demand. As a result, in 2013, we are likely to see a gap in Australia’s economic momentum....
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...Quantitative Association Rule Mining Using Information-Theoretic Approach Mary Minge University of Computer Studies, Lashio dimennyaung@gmail.com Abstract Quantitative Association Rule (QAR) mining has been recognized an influential research problem due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike Boolean Association Rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. To develop a data mining system for huge database composed of numerical and categorical attributes, there exists necessary process to decide valid quantization of the numerical attributes. One of the main problems is to obtain interesting rules from continuous numeric attributes. In this paper, the Mutual Information between the attributes in a quantitative database is described and normalization on the Mutual Information to make it applicable in the context of QAR mining is devised. It deals with the problem of discretizing continuous data in order to discover a number of high confident association rules, which cover a high percentage of examples in the data set. Then a Mutual Information graph (MI graph), whose edges are attribute pairs that have normalized Mutual Information no less than a predefined information threshold is constructed. The cliques in the MI graph represent a majority of the frequent itemsets. Keywords: Quantitative...
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...(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 in recent years. With rapid popularity of the internet, crime information maintained in web is becoming increasingly rampant. In this paper the data mining techniques are used to analyze the web data. This paper presents detailed study on classification and clustering. Classification is the process of classifying the crime type Clustering is the process of combining data object into groups. The construct of scenario is to extract the attributes and relations in the web page and reconstruct the scenario for crime mining. Key words: Crime data analysis, classification, clustering. I. INTRODUCTION Crime is one of the dangerous factors for any country. Crime analysis is the activity in which analysis is done on crime activities. Today criminals have maximum use of all modern technologies and hi-tech methods in committing crimes. The law enforcers have to effectively meet out challenges of crime control and maintenance of public order. One challenge to law enforcement and intelligence agencies is the difficulty of analyzing large volumes of data involved in criminal and...
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...Key fields are an essential part of system design since they facilitate effective organization, access, and maintenance of data structures within the system (Scott, and Rosenblatt, 2017). Primary and foreign keys are examples of the various key fields that can be utilized when designing the system. The primary keys that will be used for the patient data set include patient identification number, name, and date of birth while the secondary keys will be the provider ID number. Provider ID no will be the primary key for the primary physician data set while the patient ID, name, and date of birth will be the secondary keys. The recommended approach is advantageous over the others since it provides the patients with the flexibility to choose various...
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...Data Mining: Concepts and Techniques (3rd ed.) Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. Adapted for CSE 347-447, Lecture 1b, Spring 2015 1 1 Introduction n n n n n n n n n n Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technologies Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary 2 Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n n n Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube n n We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 3 Evolution of Sciences: New Data Science Era n n Before 1600: Empirical science 1600-1950s: Theoretical science n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding...
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...INFINITE DANGERS TO INFINITE DATA UMUC Two point five quintillion bytes of data are generated daily across the cyber world(Mora et al., 2012). With the expansion and capability to generate and store data so much so that 90% of the data stored has been generated in the last two years. (Mora et al., 2012) With the sheer volume of the data that exists and speed at which new data is generated the ability of organizational IT Staffs to meet the security and privacy requirements is being pushed to the limits. With the capability of data mining algorithms to gather and correlate such large volumes of data at such speeds there exists the potential for extreme privacy and ethical concerns; as companies become experts at slicing and dicing data to reveal details as personal as mortgage defaults and heart attack risks, the threat of egregious privacy violations grows(Waxer, 2013). The requirements to maintain the privacy and security of these vast amounts of data are both ethically and legally mandated. What are the available tool sets that are accessible to an organizations IT Staff to secure databases from intrusion and exploitation? This is of extreme importance when dealing with the volume of data that exists and the personal and private nature of so much information. There are concerns over Personally Identifiable Information (PII) as well as Personal Health Information (PHI); unauthorized access to these could lead to identity theft through the access to PII or misuse...
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...Data Mining Introduction to Management Information System 04-73-213 Section 5 Professor Mao March 22, 2011 Group 5: Carol DeBruyn, Jason Rekker, Matt Smith, Mike St. Denis Odette School of Business – The University of Windsor Table of Contents Table of Contents ……………………………………………………………...…….………….. ii Introduction ……………………………………………………………………………………… 1 Data Mining ……………………………………………………………………...……………… 1 Text Mining ……………………………………………………………………...……………… 4 Conclusion ………………………...…………………………………………………………….. 7 References ………………………………………………..……………………………………… 9 Introduction Everyday millions of transactions occur at thousands of businesses. Each transaction provides valuable data to these businesses. This valuable data is then stored in data warehouses and data marts for later reference. This stored data represents a large asset that until the advent of data mining had been largely unexploited. As companies attempt to gain a competitive advantage over each other, new data mining techniques have been developed. The most recent revolution in data mining has resulted in text mining. Prior to text mining, companies could only focus on leveraging their numerical data. Now companies are beginning to benefit from the textual data stored in data warehouses as well. Data Mining Data mining, which is also known as data discovery or knowledge discovery is the procedure that gathers, analyzes and places into perspective useful information. This facilitates the analysis of data from...
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...Contributions Data Mining Applications in Healthcare Hian Chye Koh and Gerald Tan A B S T R A C T Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions. K E Y W O R D S ■...
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