...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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...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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...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 THE BUSINESS SCHOOL, KASHMIR UNIVERSITY 5/18/2014 THE BUSINESS SCHOOL, KASHMIR UNIVERSITY 5/18/2014 Umer Rashid Roll No 55 Umer Rashid Roll No 55 Abstract: Generally, data mining (sometimes called data or knowledge discovery) 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. CRM: In today’s competitive scenario in corporate world, “Customer Retention” strategy in Customer Relationship Management (CRM) is an increasingly pressed issue. Data mining techniques play a vital role in better CRM. This paper attempts to bring a new perspective by focusing the issue of data mining Applications, opportunities and challenges in CRM. It covers the topic such as customer retention, customer services, risk assessment, fraud detection and some of the data mining tools which are widely used in CRM. Supply Chain Management (SCM) environments are often dynamic markets providing a plethora of Information, either complete or incomplete. It is, therefore, evident that such environments demand...
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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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...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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...Educational Data Mining is considered as an application of Data Mining (DM) techniques to educational data. The Main objective of Educational Data Mining is to solve the educational related issues (T. Barnes,2009). EDM explores the relationships between unique types of data produced in educational settings, which helps to understand students and the settings in which they learn. Using EDM, the institution can focus on what to teach and how to learn. Learning pattern of the students can be captured and used to develop techniques to teach...
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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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...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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...information and data. Initially, with the emergence of computers and the means for mass digital storage, we started collecting and saving all sorts of data, counting on the power of computers to help sort through this mix of information. Unfortunately, these huge collections of data stored on structures that are not similar, very quickly became too much. The production of data is expanding at an incredible rate. Expert now point to a 4300% increase in annual data generation by 2020. In 2007 the estimated information content of all human knowledge was 295 exabyte, CSC predicted that in 2020 data production will be 44 times more greater than it was in 2009....
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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 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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...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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