...1. Define data mining. Why are there many different names and definitions for data mining? Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models. Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining. What recent factors have increased the popularity of data mining? Following are some of most pronounced reasons: * More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace. * General recognition of the untapped value hidden in large data sources. * Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc. * Consolidation of databases and other data repositories into a single location in the form of a data warehouse. * The exponential increase...
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...Project Purpose This is a comprehensive project that you will work on throughout the course. You will work in groups to solve a problem using the theories, formulas, and concepts from this class. Course Objectives Execute problem-solving actions appropriate to completing a variety of case study assignments. Apply critical reading to identify the meaning of information in a problem statement. Apply analytical and logical thinking to extract facts from a problem description and determine how they relate to one another and to the problem(s) to be solved. Provide symbolic, verbal, and graphical interpretations of statements in a problem description. Apply analytical tools for evaluating the causes and potential implications of a problem. Generate potential solutions to a problem and determine the best course of action with regard to effectiveness, efficiency, and mitigation of risks. Design methodology for implementing problem solution(s). Develop tools for evaluating implementation of problem solution. Required Resources Textbook ITT Tech Virtual Library Project Logistics Select ONE of the following three projects: A, B, or C. You may work individually or in a group. Because of the workload, working in groups is recommended. Working as an individual on this project is discouraged. Project Deliverables Four written reports Final report Project presentation (Unit 10) Each written report must have the following items: APA formatting, double-spaced...
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...2. Data mining search parameters A data mining algorithm is a set of heuristics and calculations that creates a data mining model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics. The mining model that an algorithm creates from your data can take various forms, including: * A set of clusters that describe how the cases in a dataset are related. * A decision tree that predicts an outcome, and describes how different criteria affect that outcome. * A mathematical model that forecasts sales. * A set of rules that describe how products are grouped together in a transaction, and the probabilities that products are purchased together. Microsoft SQL Server Analysis Services provides multiple algorithms for use in your data mining solutions. These algorithms are implementations of some of the most popular methodologies used in data mining. All of the Microsoft data mining algorithms can be customized and are fully programmable using the provided APIs, or by using the data mining components in SQL Server Integration Services. You can also use third-party algorithms that comply with the OLE DB for Data Mining specification, or develop custom algorithms that can be...
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...Project 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...
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...McPhee and Tony Hughes regarding how the risk of these two projects should be measured and incorporated into the investment evaluation process. Are both of them technically correct in the methods they suggest to account for project risk, and which method of risk-adjustment do you think should be applied in evaluating the feasibility of these two projects? As defined by Mira and Dunja, 2005, risk can be determined as knowing future event probability, and uncertainty as unknown probability of future events. Measured uncertainty is a risk. Term risk and uncertainty are often used as synonyms in economy because there is no possibility in economy of some event repetition in exactly the same circumstances. That means that it is hard to measure risk, and event’s probability is a highly subjective estimation. The term risk prevails in portfolio analysis whether in the sense of risk measure (e.g. beta is the risk measure) or in the sense of uncertainty. A project can be defined as an entity of inter-dependent activities, which is unique and has its purpose and objective. In this case study, we have two mutually exclusive investment projects. The first prospective investment involved a strip (open-cut) mining operation in western New South Wales. The second investment also involved the extraction of coal, but this expenditure would be an underground site in South-Eastern Victoria. Based on Mira and Dunja, 2005, project risk can be defined as the cumulative effect of the chances...
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...Original 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....
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...[BI-PROJECT REPORT] April 13, 2014 DATA MINING Analysis of Bike sharing dataset April 13, 2014 Group 007 MIS 6324 1 [BI-PROJECT REPORT] April 13, 2014 Project Report for Analysis of bike sharing dataset MIS-6324 Intro. to business intelligence software and techniques Prepared by Group Name Group007 Group Members Rohith Raj Abhay Joshi Sai Karan Jahnavi Papanaboina Under the guidance of Professor Kelly Slaughter, PhD Clinical Professor Information Systems University of Texas at Dallas MIS 6324 2 [BI-PROJECT REPORT] April 13, 2014 Table of Contents 1.Introduction to Data Mining ...................................................................................................................... 4 2. Background of the dataset ........................................................................................................................ 4 2.1 Description of dataset ......................................................................................................................... 5 3.Outline of Analysis ..................................................................................................................................... 6 4. The Methodology ...................................................................................................................................... 7 5. Pre-processing the dataset ...........................................................................................................
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...Introduction 2 Assumptions 3 Data Availability 3 Overnight processing window 3 Business sponsor 4 Source system knowledge 4 Significance 5 Data warehouse 6 ETL: (Extract, Transform, Load) 6 Data Mining 6 Data Mining Techniques 7 Data Warehousing 8 Data Mining 8 Technology in Health Care 9 Diseases Analysis 9 Treatment strategies 9 Healthcare Resource Management 10 Customer Relationship Management 10 Recommended Solution 11 Corporate Solution 11 Technological Solution 11 Justification and Conclusion 12 References 14 Health Authority Data (Appendix A) 16 Data Warehousing Implementation (Appendix B) 19 Data Mining Implementation (Appendix B) 22 Technological Scenarios in Health Authorities (Appendix C) 26 Technology Tools 27 Data Management Technology Introduction The amount of information offered to us is literally astonishing, and the worthiness of data as an organizational asset is widely acknowledged. Nonetheless the failure to manage this enormous amount of data, and to swiftly acquire the information that is relevant to any particular question, as the volume of information rises, demonstrates to be a distraction and a liability, rather than an asset. This paradox energies the need for increasingly powerful and flexible data management systems. To achieve efficiency and a great level of productivity out of large and complex datasets, operators need have tools that streamline the tasks of managing the data and extracting valuable...
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...located in Sydney Australia; it is classified as a heavy construction company by Dow Jones Industry, Leighton holding’s main operating activities include: project construction, project management, property development and contract mining services. The diversified services were provided by different and independent operating companies owned by Leighton Holdings; those are companies such as Leighton Contractor, John Holland, Leighton Asia, India and Offshore, Thiess, Leighton Properties and Habtoor Leighton group. These independent operating companies provide diversified services from construction, development, contract mining, operation and maintenance service to the resources, infrastructure and property markets. Geographically, Leighton Holdings Ltd’s business operation covers over 20 countries in Australia, Asia, Gulf area and Southern Africa. On the global scale, Leighton Holdings is No.6 largest heavy construction company by sales in the world in 2012. Its total revenue reached USD 19587.3 millions with an employee team of 56000 people, the company is listed in Australian Stock exchange and stock code is LEI. 2) Corporate business model Diversification is the core of Leighton Holdings business model, the company is aiming to take on their core competencies to the targeted markets and deliver the project and value-added project management services to their clients through Leighton Holding’s diversity teams and financial strength. The reason for the company to choose diversify...
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...Unit 6 Assignment 1: MyFoundationsLab Module 5 Learning Objectives and Outcomes Apply analytical and logical thinking to extract information. Assignment Requirements Log into MyFoundationsLab, and complete the Skills Assessment in Module 5: Ratio, Proportion and Percent. Note: You are encouraged to work ahead in MyFoundationsLab. If you finish a module early, or if there is an assignment that refers to a later module, feel free to move ahead. All modules must be complete by the end of the quarter to give you the best chance of success in follow-on courses. Required Resources Textbook MyFoundationsLab Submission Requirements Print a screen shot showing completion of the Skills Assessment in Module 5 of MyFoundationsLab. Grading Rubric 0 points if you don’t turn in your screenshot. 50 points for completing your assessment and turning in your screen shot. 100 points for completing your assessment, earning a score higher than 75, and turning in your screen shot showing your gold star for Module 5. Note, there are three ways you can show mastery, and each of those will result in a gold star on the module in the Learning Path. First, you can take and pass the Skills Check. Second, you can work through all the topics that are recommended for you. Finally, you can take and pass the post test. All of these methods will generate the gold star on your Learning Path. Unit 6 Problem Set 1: Blimp Exercise Learning Objectives and Outcomes Apply analytical...
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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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...and Lifecycle Erik Perjons, DSV, SU/KTH perjons@dsv.su.se The data warehouse architecture The back room The front room Analysis/OLAP Productt Product2 Product3 Product4 Time1 Time2 Time3 Time4 Value1 Value2 Value3 Value4 Value11 Value21 Value31 Value41 Data warehouse External sources Extract Transform Load Serve Query/Reporting Operational source systems Data marts Data mining Falö aöldf flaöd aklöd falö alksdf Operational source Data staging systems (RK) area (RK) Legacy systems Back end tools OLTP/TP systems Data presentation area (RK) ”The data warehouse” Presentation (OLAP) servers Data access tools (RK) End user applications Business Intelligence tools Operational Source Systems Operational source systems characteristics: Operational source systems • the source data often in OLTP (Online Transaction Processing) systems, also called TPS (Transaction Processing Systems) • high level of performance and availability • often one-record-at-a time queries • already occupied by the normal operations of the organisation OLTP vs. DSS (Decision Support Systems) OLTP vs. OLAP (Online analytical processing) Operational Source Systems More operational source systems characteristics: Operational source systems • a OLTP system may be reliable and consistent, but there are often inconsistencies between different OLTP systems • different types of data format and data structures in different OLTP systems AND DIFFERENT SEMANTICS Operational...
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...UNIVERSITY OF MUMBAI Bachelor of Engineering Information Technology (Third Year – Sem. V & VI) Revised course (REV- 2012) from Academic Year 2014 -15 Under FACULTY OF TECHNOLOGY (As per Semester Based Credit and Grading System) University of Mumbai, Information Technology (semester V and VI) (Rev-2012) Page 1 Preamble To meet the challenge of ensuring excellence in engineering education, the issue of quality needs to be addressed, debated and taken forward in a systematic manner. Accreditation is the principal means of quality assurance in higher education. The major emphasis of accreditation process is to measure the outcomes of the program that is being accredited. In line with this Faculty of Technology of University of Mumbai has taken a lead in incorporating philosophy of outcome based education in the process of curriculum development. Faculty of Technology, University of Mumbai, in one of its meeting unanimously resolved that, each Board of Studies shall prepare some Program Educational Objectives (PEO‟s) and give freedom to affiliated Institutes to add few (PEO‟s) and course objectives and course outcomes to be clearly defined for each course, so that all faculty members in affiliated institutes understand the depth and approach of course to be taught, which will enhance learner‟s learning process. It was also resolved that, maximum senior faculty from colleges and experts from industry to be involved while revising the curriculum. I am happy to state...
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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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...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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