...important features of data mining tools Data mining is the process of fetching hidden information from huge databases for the purpose of analysis. Basically, it is a method to search for information that can prove to be useful for an organisation and to extract that knowledge from very lengthy and large databases. It uses a variety of statistical algorithms and analysis techniques to derive results. Although, this might sound easy but data mining is a lengthy process and requires loads of time and patience. It requires a lot of man-hours as an application can mine the data from the databases but it is the responsibility of the human to describe the data to look for to the application and also to find and collect the databases. (Naxton, n.d.) Analysis is key to outperforming your competition in today’s world. Almost all businesses rely on data to figure out the future market trends, know more about their customers and their preferences etc. An example of data mining is why companies advertise on Facebook as they get to reach a vast audience and learn about their habits. The information is derived from the advertisements the people click on, the time spent on that specific advert, the type of adverts they hide or like, and all this data is of value to companies to understand the market. Data mining comprises of 5 elements (“Data Mining—Why is it Important?,” n.d.): • “Extract, transform, and load transaction data onto the data warehouse system” • Store data in a MDB system to...
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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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...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 Nabeel Ahmed University of Northern Virginia Abstract ‘The vein of research data is almost always richer than it appears to be on the surface, but it can only be of value if mined.—Morris Rosenberg’ (AGOSTA, 2000) Recent years, Data Mining has become hot topic of enterprises. More and more companies intend to introduce data mining techniques. One report from the United States treats data mining as one of the ten favorable fields in the 21st century, of which by means shows its importance. Generally speaking, data mining are often applied in those fields, such as insurance and finance industries, retailing and direct marketing industries, communication industry, manufacturing industry and Medical service industry, etc. The data related to management decision making has been accumulating surprisingly quickly because of the improvement in high technology. As the byproduct of internet, e-commerce, e-banking, pos system, barcode scanner and intelligent robot, the acquirement of electronic data has already become cheap and existing everywhere. These data are normally stored in data warehouse and data marts to provide assistance for management decision-making. Data mining is a fast growing field, its main target is to develop some techniques to assist the managers in intelligent analyzing and utilizing mass data. Data mining was already being reported in successfully utilized in the aspects of credit rating, fraud detection, database marketing, customer relationship...
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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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...Text Mining For Gold 1) What is the business impact of text mining? What problems does it solve? Text mining is the discovery of patterns and relationships from large sets of unstructured data; such as text files, emails, memos, call center transcripts, survey responses, legal cases, patent descriptions, and service reports. Text mining and text mining tools help businesses analyze this data (Laudon 164). The tools are able to extract the key elements from large unstructured data sets, discover patterns and relationships and summarize the information. Businesses use these tools to analyze transcripts of calls to customer service centers to identify major service and repair issues. The problems that are solved with text mining is; it shortens the time to accurately find data. By converting unstructured text into structure output, text mining results can feed into further analytics or be combined with the results of other data analyses. By doing so it enables delivery of comprehensive, high quality text mining results as part of systematic and reproducible workflows. 2) How does text mining improve operational efficiency and decision making? Text mining improves efficiency and decision making by providing the tools such as software so that companies can choose what data they want to focus on. Text mining software is starting to get popular and software companies are developing software to accommodate business needs. Example, the Law Firm DLA Piper discussed in...
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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 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 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 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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...Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology, if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining. Keywords: data mining, benefits, privacy concerns Data Mining Benefits of Data Mining for a Business Data mining can be explained as the process of a business collecting data on their customers or potential customers to increase customer business. A business will collect data on their customers or potential customers and use that data to give them coupons, promote sells, and analyze buying and selling trends. Data mining can benefit the customer as well as the business. Data mining can be used in the retail industry, the finance industry, and the healthcare industry. Any industry can benefit from data mining but those are the top three (Turban & Volonino, 2011). Data mining is a way for large businesses to get to know their customers. The information gathered from data mining can let a large company learn what their customers want and how they want it. It can also benefit large companies get to know their employees, the company can learn how to satisfy their...
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...What is data mining: * Data mining (knowledge discovery from data) * Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data * data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large preexisting databases; a way to discover new meaning in data. 2. KDD process * General functionality * Descriptive data mining * Predictive data mining * Different views lead to different classifications * Data view: Kinds of data to be mined * Knowledge view: Kinds of knowledge to be discovered * Method view: Kinds of techniques utilized * Application view: Kinds of applications adapted Data mining issues * Mining methodology * Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web * Performance: efficiency, effectiveness, and scalability * Pattern evaluation: the interestingness problem * Incorporation of background knowledge * Handling noise and incomplete data * Parallel, distributed and incremental mining methods * Integration of the discovered knowledge with existing one: knowledge fusion * User interaction * Data mining query languages and ad-hoc mining * Expression and visualization of data mining results * Interactive mining of knowledge at multiple...
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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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...[pic] Data Mining Assignment 4 [pic] “Data mining software is one of a number of analytical tools for analyzing data (Data Mining, para. 1).” We will be learning about the competitive advantage, reliability of such tool, and privacy concerns towards consumers. Data mining tool is used by majority of companies to increase revenue, and build on the relationship with current consumers. Let’s explore the world of data mining technology in the following selection. “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 (Data Mining, para. 7).” Data mining is implemented online to promote business ideas, products, and other ways to market them. Data mining is used in political websites, when you go to some sites they take your information then, they began to send you things to promote the Republicans and Democrats message. This is how your voice counts. “Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research...
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...Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management) © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation Mining Large Data Sets - Motivation There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all 4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1995 1996 1997 The Data Gap Total new disk (TB) since 1995 Number of analysts 1998 1999 4 © Tan,Steinbach, KumarKamath, V. Kumar, “Data Mining for Mining and Engineering Applications”...
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