...Report – Webcast 8/13/14 on Data Mining SAS (Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field...
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...Report – Webcast 8/13/14 on Data Mining SAS (Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field...
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...CHAPTER 2 DATA MINING TECHNIQUE OVERVIEW 2.1 Introduction In the 21st century as we are moving towards more and more online system, the databases have grown into terabytes. Within this huge data, information of importance needs to be identified. Since the evolution of human life, the people discover patterns. As farmer recognizes pattern of growth in the field, bank recognizes the earning and spending pattern of a customer and politicians seeks pattern in voter opinion. This huge amount of data needs to be used either for business growth or scientific discoveries. The process of discovering the patterns and relationships in data using the analysis tools is called Data Mining. The simplest form of data mining is as follows: 1. Describing...
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...students in tertiary institutions has for a long time been the focus of study among higher education managers, parents, government and researchers. The cause of this differential can be due to intellective, non-intellective factors or both. From studies investigating student performance and related problems it has been determined that academic success is dependent on many factors such as; grades and achievements, personality and expectations, and academic environments. This work uses data mining techniques to investigate the effect of socio-economic or family background on the performance of students using the data from one of the Nigerian tertiary institutions as case study. The analysis was carried out using Decision Tree algorithms. The data comprised of two hundred forty (240) records of students. The academic performance of students was measured by the students’ first year cumulative grade point average (CGPA). Various Decision Tree algorithms were investigated and the algorithm which best models the data was used to generate rule sets which can be used to analyze the effect of the socio-economic background of students on their academic performance. The rules generated can serve as a guide to educational administrators in their planning activities. Keywords: Socio-Economic, Performance. Intellective, Family Background, Academic 1.0 INTRODUCTION The differential students’ performance in tertiary institutions has been and is still a source of great concern and research interest...
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...(YourFirstName, YourLastName) University Name Data Modeling While the importance of data mining huge volumes of data from expansive volumes of data cannot be gainsaid, there are several shortcomings of data mining as outlined in the US Government’s General Accounting Data Mining Report. The unearthed findings are discussed below. Nascent Data Mining Efforts It is reported that out of the 128 federal departments and agencies surveyed on their use of data mining, it can be revealed that only 52 agencies are using or are planning to use data mining. This means that more than half of the government’s departments are yet to harness the power of data mining. The implementation of data mining poses a variety of challenges such as due to human factors like the learning curve effect, spelling and referencing mistakes as well as systematic factors such as incoherent logic and system failures. The federal government outsources the initial implementation process to third party specialists and this poses the threat of sensitive information finding its way to malicious nosey individuals like the Edward Snowdens of this world. As such, these privacy concerns are valid. Classification Of paramount importance is the arranging and grouping of data into meaningful classes if the information to be generated from the data mining is to be of any sense. For example, there is the likelihood that Grantee Monitoring Activities Offices in the Department of Agriculture do not incorporate...
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...in a Requirement Life-Cycle Framework Abstract In this paper, a requirements-based framework of innovation is discussed. Both customer and expert defined requirements are considered. The proposed framework treats requirements as evolving entities and is implemented using a data-driven approach. It provides a new perspective in support of the innovative product development process. Keywords: Innovation, requirements management, evolutionary computation, data mining. 1. Introduction The volume of information entering a corporate decision-making landscape is increasing. Not too long ago, corporate business models were based on information asymmetry, neglecting the customer needs. Customers did not have the full information about the products available to them. With the creation of internet, the information revolution was bound to happen. Nowadays, a customer can literally search any product available in the global market. This search is usually based on variety of requirements ranging from functional to emotional. Companies can not neglect analysis of customer needs. Therefore, the ability to successfully translating customer requirements in the product development process is of paramount importance, if not the most important one. What is the best business model in today’s competitive market? This is the question that constantly haunts business leaders who are at the front-end of competition as well as the research communities who are continuously developing the successful...
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...Republic of the Philippines OCCIDENTAL MINDORO STATE COLLEGE Rizal Street, San Jose, Occidental Mindoro Website: www.omsc.edu.ph Email address: omsc_9747@yahoo.com Telefax No.: (043) 491-1460 COLLEGE OFARTS, SCIENCES AND TECHNOLOGY VISION: OCCIDENTAL MINDORO STATE COLLEGE is envisioned to be an agent of change for the development of the total person responsive to the challenges of globalization. MISSION: To train and develop a new breed of highly competitive, innovative, resourceful and values-oriented graduate through quality instruction, relevant research, community based extension and sustainable production. Department Goal: The Information Technology shall provide its students with the necessary knowledge, values and skills through research – basedendeavor in order to prepare them to meet the demands and challenges of the time. Program: BACHELOR OF SCIENCE IN INFORMATION TECHNOLOGY Program Objectives: The BS Information technology program includes the study of the utilization of both hardware and software technologies involving planning, installing, customizing, operating, managing and administering, and maintaining information technology infrastructure that provides computing solutions to address the needs of an organization. The program prepares graduates to address various users’ needs involving the selection, development, application, integration and management of computing technologies within an organization Course Title: Free Elective II...
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...Top 10 data mining algorithms in plain English 1.1K Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining. What are we waiting for? Let’s get started! Contents [hide] 1. C4.5 2. k-means 3. Support vector machines 4. Apriori 5. EM 6. PageRank 7. AdaBoost 8. kNN 9. Naive Bayes 10. CART Interesting Resources Now it’s your turn… Update 16-May-2015: Thanks to Yuval Merhav and Oliver Keyes for their suggestions which I’ve incorporated into the post. Update 28-May-2015: Thanks to Dan Steinberg (yes, the CART expert!) for the suggested updates to the CART section which have now been added. 1. C4.5 What does it do? C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified. Wait, what’s a classifier? A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. What’s an example of this? Sure, suppose a dataset contains a bunch of patients. We know various things about each patient like age, pulse...
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...of collected data. The presence of outliers may indicate something sinister such as unauthorised system access or fraudulent activity, or may be a new and previously unidentified occurrence. Whatever the cause of these outliers, it is important they are detected so appropriate action can be taken to minimise their harm if malignant or to exploit a newly discovered opportunity. Chandola, Banerjee and Kumar (2007) conducted a comprehensive survey of outlier detection techniques, which highlighted the importance of detection across a wide variety of domains. Their survey described the categories of outlier detection, applications of detection and detection techniques. Chandola et al. identified three main categories of outlier detection - supervised, semi-supervised and unsupervised detection. Each category utilises different detection techniques such as classification, clustering, nearest neighbour and statistical. Each category and technique has several strengths and weaknesses compared with other outlier detection methods. This review provides initial information on data labelling and classification before examining some of the existing outlier detection techniques within each of the three categories. It then looks at the use of combining detection techniques before comparing and discussing the advantages and disadvantages of each method. Finally, a new classification technique is proposed using a new outlier detection algorithm, Isolation Forest. DATA LABELLING Datasets...
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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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...Title Use of Data mining by government agencies and practical applications (Describe the Data Mining technologies, how these are being used in government agencies. Provide practical applications and examples) Compiled By:- Sneha Gang (Student # - 84114) Karan Sawhney (Student # - 85471) Raghunath Cherancheri Balan (Student # - 86088) Sravan Yella (Student # - 87041) Mrinalini Shah (Student # - 86701) Use of Data mining by government agencies and practical applications * Abstract (Sneha Garg) With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is a process of inferring knowledge from such huge data. It is a modern and powerful tool, automatizing the process of discovering relationships and combinations in raw data and using the results in an automatic decision support. This project provides an overview of data mining, how government uses it quoting some practical examples. Data mining can help in extracting predictive information from large quantities of data. It uses mathematical and statistical calculations to uncover trends and correlations among the large quantities of data stored in a database. It is a blend of artificial intelligence technology, statistics, data warehousing, and machine learning. These patterns play a very...
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...exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration into a single repository such as a data warehouse or data mart. The analysis is typically...
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...Data mining and warehousing and its importance in the organization * Data Mining Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to drill down into summary information to view detail transactional data. For example, “Entertainers Incorporated” is an organization which deals with entertainers for events. So the need to attract customers and communicating with them is essential. Customer satisfaction in their service is much needed for them, for the customers to approach them for the next event too. So considering all...
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...An Overview of Data Mining Techniques Page 1 of 48 An Overview of Data Mining Techniques Excerpted from the book Building Data Mining Applications for CRM by Alex Berson, Stephen Smith, and Kurt Thearling 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 be able to understand how each algorithm fits into the landscape of data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems. I. Classical Techniques: Statistics, Neighborhoods and Clustering 1.1. The Classics These two sections have been broken up based on when the data mining technique was developed and when it became technically mature enough to be used for business, especially for aiding in the optimization of customer relationship management systems. Thus this section contains descriptions of techniques that have classically been used for decades the next section represents techniques...
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...Data Mining Professor Clifton Howell CIS500-Information Systems Decision Making March 7, 2014 Benefits of data mining to the businesses One of the benefits to data mining is the ability to utilize information that you have stored to predict the possibilities of consumer’s actions and needs to make better business decisions. We implement a business intelligence that will produce a predictive score for those consumers to determine these possibilities. Predictive analytics is the business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization. (Impact, 2014) The usefulness of predictive scoring is obvious. However, with no predictive model and no means to score your consumer, the possibility of gaining a competitive edge and revenue is also predictable. To discover consumer buying patterns from a transaction database, mining association rules are used to make better business decisions. However because users may only be interested in certain information from this database and do not want to invest a lot of time in searching for what they need, association discovery will assist in limiting the data to which only the end user needs. Association discovery will utilize algorithms to lessen the quantity of groupings of item sets or sequences in each customer...
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