...with the Use of Data Mining An emergent Information Technology (IT) issue that has been rising in the past few years has been data mining. Data mining is utilized to retrieve personal identifiable information provided by individuals through the use of Internet services such as: social media networks, email, and other networks that contain data bases full of personal information. If such data retrieval if not done careful, it can cause ethical issues for the companies that are involved. The ethical issues related to data mining are violation of privacy, confidentiality, and respect of persons’ rights. Issues that required the immediate attention regarding data mining are: What stops corporations from sharing personal identifiable information with other companies?; How effectively and ethically data mining is use by the government?; Is our privacy and confidentiality truly protected? Social network companies such as Facebook, Twitter, and Google provide users agreements upon joining their services. These agreements underline how the information provided by the user will be utilize by the company and it allows the user to understand how to protect their personal identifiable information while utilizing these social network sites. These companies pride themselves in protecting users’ personal information. However, what happens when the company or an unethical company employee violates these agreements? Personal identifiable information is then released to use as it pleases...
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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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...Data Mining D t Mi i Module 1 Introduction to Data Mining Dr. Jason T.L. Wang, Professor Department of Computer Science New Jersey Institute of Technology / Data Management: Its Evolution 1960s: – File management and network DBMS 1970s: – Relational DBMS 1980s: 980s – Non-first normal form, extended-relational, OO, deductive databases and application-oriented DBMS pp (spatial, scientific, CAD/CAM, etc.) 1990s - present: p – Data mining, digital library, and Web databases – Cloud databases, data science, and Big Data Data Mining © Jason Wang 2 Data Mining: Its Definition Data mining (knowledge discovery in databases): ) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names: – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, analysis data archeology, data dredging archeology dredging, information harvesting, etc. Data Mining © Jason Wang 3 Data Mining: A Multidisciplinary Field Pattern Recognition Machine Learning Databases St ti ti Statistics Information Visualization Data Mining © Jason Wang 4 Data to be mined Text databases Web databases Scientific and biological databases Transactional databases Data Mining © Jason Wang 5 Knowledge to be discovered K l d t b di d Association (correlation) ...
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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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...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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...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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...Data Mining Running head: DATA MINING Data Mining and How It Relates to Information Systems Carmelisa Cummings Strayer University Abstract Data mining is the process of analyzing data to remove information not offered by the unprocessed data alone. Data mining systems sort instantly through the information to discover patterns and relationships that would elude an army of human researchers. Data-mining tools relate algorithms to information sets to discover inherent trends and patterns in the information, which analysts use to create new business strategies. Data mining is exploring and analyzing detailed business transactions. It implies "digging through tons of data" to uncover patterns and relationships contained within the business activity and history. Data mining can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyze the data automatically. Data mining has become an important part of customer relationship management (CRM). In order to better understand customer behavior and preferences, businesses use data mining to wade through the huge amounts of information gathered via the Web. (Answers.com, 2009) How does data mining work? While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data...
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...In today’s business environment, businesses must be able to sift through and analyze massive amounts of data to gain a competitive edge over their competition. Utilizing data mining techniques, businesses are given the ability to analyze data from different points of view and turn it into useful information that can be used to increase revenue, cut costs, or both (Jason.Frand, n.d.). In today’s environment, competitive businesses use what is known as “Predictive Analytics” to perform mining and analysis of their data. In fact, predictive analytics is a form of data mining that if used properly can automatically sort and index a company database to create a predictive model based off corporate knowledge (Eric Siegel, 2005). Predictive Analytics use business intelligence technology to produce a score known as a predictor, which is a measurable value for every customer or organizational element. Once data records such as where, when, and how purchases are made are correlated, a predictive predictor or score is created. This predictor, in conjunction with other information, can assist in informing businesses what actions to take in order to get the consumer to purchase the goods they are offering. In fact, the proper utilization of predictive analytics can optimize marketing campaigns, improve web site behavior, reduce customer response times, increase revenue, and cut costs. The way companies and customers interact and perform their daily business has changed throughout the years...
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...512 Use of Data Mining in the field of Library and Information Science : An Overview Roopesh K Dwivedi Abstract Data Mining refers to the extraction or “Mining” knowledge from large amount of data or Data Warehouse. To do this extraction data mining combines artificial intelligence, statistical analysis and database management systems to attempt to pull knowledge form stored data. This paper gives an overview of this new emerging technology which provides a road map to the next generation of library. And at the end it is explored that how data mining can be effectively and efficiently used in the field of library and information science and its direct and indirect impact on library administration and services. R P Bajpai Keywords : Data Mining, Data Warehouse, OLAP, KDD, e-Library 0. Introduction An area of research that has seen a recent surge in commercial development is data mining, or knowledge discovery in databases (KDD). Knowledge discovery has been defined as “the non-trivial extraction of implicit, previously unknown, and potentially useful information from data” [1]. To do this extraction data mining combines many different technologies. In addition to artificial intelligence, statistics, and database management system, technologies include data warehousing and on-line analytical processing (OLAP), human computer interaction and data visualization; machine learning (especially inductive learning techniques), knowledge representation, pattern recognition...
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...Data Mining Jenna Walker Dr. Emmanuel Nyeanchi Information Systems Decision Making May 30, 2012 Abstract Businesses are utilizing techniques such as data mining to create a competitive advantage customer loyalty. Data mining allows business to analyze customer information, such as demographics and purchase history for a better understanding of what the customers need and what they will respond to. Data mining currently takes place in several industries, and will only become even more widespread as the benefits are endless. The purpose of this paper is to gain research and examine data mining, its benefits to businesses, and issues or concerns it will need to overcome. Real world case studies of how data mining is used will also be presented for a deeper understanding. This study will show that despite its disadvantages, data mining is an important step for a business to better understand its customers, and is the future of business marking and operational planning. Tools and Benefits of data mining Before examining the benefits of data mining, it is important to understand what data mining is exactly. Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends...
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...Data mining is an iterative process of selecting, exploring and modeling large amounts of data to identify meaningful, logical patterns and relationships among key variables. Data mining is used to uncover trends, predict future events and assess the merits of various courses of action. When employing, predictive analytics and data mining can make marketing more efficient. There are many techniques and methods, including business intelligence data collection. Predictive analytics is using business intelligence data for forecasting and modeling. It is a way to use predictive analysis data to predict future patterns. It is used widely in the insurance, medical and credit industries. Assessment of credit, and assignment of a credit score is probably the most widely known use of predictive analytics. Using events of the past, managers are able to estimate the likelihood of future events. Data mining aids predictive analysis by providing a record of the past that can be analyzed and used to predict which customers are most likely to renew, purchase, or purchase related products and services. Business intelligence data mining is important to your marketing campaigns. Proper data mining algorithms and predictive modeling can narrow your target audience and allow you to tailor your ads to each online customer as he or she navigates your site. Your marketing team will have the opportunity to develop multiple advertisements based on the past clicks of your visitors. Predictive...
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...What Is Data Mining? Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). The key properties of data mining are: * Automatic discovery of patterns * Prediction of likely outcomes * Creation of actionable information * Focus on large data sets and databases Data mining can answer questions that cannot be addressed through simple query and reporting techniques. Automatic Discovery Data mining is accomplished by building models. A model uses an algorithm to act on a set of data. The notion of automatic discovery refers to the execution of data mining models. Data mining models can be used to mine the data on which they are built, but most types of models are generalizable to new data. The process of applying a model to new data is known as scoring. See Also: Oracle Data Mining Application Developer's Guide for a discussion of scoring and deployment in Oracle Data Mining Prediction Many forms of data mining are predictive. For example, a model might predict income based on education and other demographic factors. Predictions have an associated probability (How likely is this prediction to be true?). Prediction probabilities are also known as confidence (How confident can I be of this prediction...
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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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...HP and Text Mining What is the pratical application of text mining: The practical application of text mining really is the combination of structured and unstructured data. Text mining applications pull data from sources such as word documents and emails in the form of texts, filters the data, and then translates it into a format the can be analyzed and recorded. Without text mining, written texts and other unstructured data would really become worthless data sources. According to studies done on BI and data mining, businesses and other BI clients are looking more and more to unstructured data as a primary data source. Probably the most pratcial application of text mining would have to be marketing. How do you think text mining techniques could be used in other businesses: There is almost a limitless amount of applications for text mining in other businesses. The most obvious use of text mining for other businesses would be to analyze written customer reviews and/or comments. Essentially, Text Mining can be used anywhere where there is a direct and free form line of communication between an entity and its actors. In the past, only a human was able to read, translate, record, and respond to these lines of communications. Text mining allows these processes to be completed without any human assistance. This means that new divisions and processes within a company could become automated such as responses to customer inquiries. What were HP’s challenges in...
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...Data Mining 6/3/12 CIS 500 Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. This information can be used to increase revenue, cut costs or both. Data mining software is a major analytical tool used for analyzing data. It allows the user to analyze data from many different angles, categorize the data and summarizing the relationships. In a nut shell data mining is used mostly for the process of finding correlations or patterns among fields in very large databases. What ultimately can data mining do for a company? A lot. Data mining is primarily used by companies with strong customer focus in retail or financial. It allows companies to determine relationships among factors such as price, product placement, and staff skill set. There are external factors that data mining can use as well such as location, economic indicators, and competition of other companies. With the use of data mining a retailer can look at point of sale records of a customer purchases to send promotions to certain areas based on purchases made. An example of this is Blockbuster looking at movie rentals to send customers updates regarding new movies depending on their previous rent list. Another example would be American express suggesting products to card holders depending on monthly purchases histories. Data Mining consists of 5 major elements: • Extract, transform, and load transaction data onto the data...
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