...Running Head: DATA MINING Assignment 4: Data Mining Submitted by: Submitted to: Course: Introduction Data Mining is also called as Knowledge Discovery in Databases (KDD). It is a powerful technology which has great potential in helping companies to focus on the most important information they have in their data base. Due to the increased use of technologies, interest in data mining has increased speedily. Data mining can be used to predict future behavior rather than focus on past events. This is done by focusing on existing information that may be stored in their data warehouse or information warehouse. Companies are now utilizing data mining techniques to assess their database for trends, relationships, and outcomes to improve their overall operations and discover new ways that may permit them to improve their customer services. Data mining provides multiple benefits to government, businesses, society as well as individual persons (Data Mining, 2011). Benefits of data mining to the businesses when employing Advantages of data mining from business point of view is that large sizes of apparently pointless information have been filtered into important and valuable business information to the company, which could be stored in data warehouses. While in the past, the responsibility was on marketing utilities and services, products, the center of attention is now on customers- their choices, preferences, dislikes and likes, and possibly data mining is one of the most important tools...
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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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...ADVANCES BUSINESS ANALISIS Introduction : We can answers to what happene,d why, what is happening. But difficlut to answer what will happen ? and we can often discover uncexpected connection in the business ! Data mining is defi ned as “the nontrivial extraction of implicit, previously unknown, and potentially useful information (patterns) from data.” This is called knowledge discovery. The most important thing is to identify the patterns, whcich allow us to deine the structure ; We can say tjat data mining gives us knowledge. The most common application of data mining are : classification, prediction, cluster analisis (objetcs that have similars featurs) , mining association rules 1) Classification : trees Ex : the decision to grant credit How you construct a classifier : learning on the basis of learning eexamples (examples of correctly categorized objects) it gives us learning system (algorytms) and then classifier. Limitations : To construct a classifi er on the basis of a set of examples, you need to solve many problems that are common for the majority of data-mining algorithms. However, if you are aware of these limitations, you should have reasonable expectations regarding their possible applications and the quality of the knowledge generated by them. The main problems are connected with induction, history, updating, and overfi tting : * Induction problem : learning from examples is inductive reasoning : so we make generalizacion, from limited observation...
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...Assignment 4: Data Mining CIS 500 Dr. Besharatian Submitted by: Eric Spurbeck December 7, 2013 Abstract This paper will discuss the process of data mining, how it is used, for what purpose it is used and what information can be gathered from the data, which is compiled from data mining. Assignment 4: Data Mining Webopedia (2013) defines data mining as, "A class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests." This means that large groups of data that is derived by information obtained through customers, customer purchases and customer buying habits. Businesses use this information for a variety of reasons; it is used for purchasing merchandise, tracking how certain merchandise is selling and even customers buying habits. Webopedia goes on to state that "data mining is popular in the science and mathematical fields but also is utilized increasingly by marketers trying to distill useful consumer data from Web sites." Predictive analytics are used to understand customer's behaviors, according to the article Predictive Analytics with Data Mining: How It Works (Siegel, Feb. 2005) it describes how this method has a predictor. This is "a single value measured for each customer" this is based on the customers purchased over a period and sets higher values for the most recent customer purchases. The...
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...Project on Data Management Submitted By: Name……………………. Section………………….. Question1. What are the components of quality data? Answer1. The latest information technology dimensions has enabled to make diverse use of data and turning the raw data into meaningful information to extract the quality. Data can be referred to raw numbers, figures which are useless unless they are put into a form and converted into a useful information. A data is said to be effective when it is converted into useful form or rather can be used to provide some information. When the raw data which is numeric, figures is converted into meaningful information, it is said to be known as information which can be used to give the data a meaning. Data quality is defined by its usefulness. When the data offers accurate information regarding a person or an organization then it is said to be known as data quality. The components of data quality can be analyzed with the help of the following data elements: 1. Accuracy which defines how correct and precise is the data 2. Completeness, the comprehensiveness of data also defines the data quality 3. Timeliness, the timely updates on the data also ensures that the data is correct and free from any discrepancy 4. Relevancy, the data that is gathered should be relevant which defines the purpose and fulfills the use. It defines that the data gathered accomplish the purpose 5. The last element of data quality is its availability. It defines the availability...
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...present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated. Keywords : Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitoring Introduction Environmental Information Systems (EIS) is a generic term that describes the class of systems that perform one or more of the following tasks: environmental monitoring, data storage and access, disaster description and response, environmental reporting, planning and simulation, modeling and decision- making. As the requirements for accurate and timely information in these systems are increasing, the need for incorporating advanced, intelligent features in EIS is revealed. In this context advances in Information Technology (IT) sector are promising to satisfy these requirements. Enviromatics (an abbreviation of the term “environmental informatics”) is the research initiative examining the application of Information Technology in environmental...
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...an entire research library. Page 266 Terabytes 3) The equivalent amount of all the words ever spoken by human beings is approximately: Page 266 5 Exabytes 4) The systems that use sophisticated statistical analysis to process data are known as: Page 267 Data-mining tools 5) ________ systems are programs that read data from a variety of sources, process that data and produce and deliver formatted reports to users. Page 267 Reporting 6) In most cases, data-mining tools are used to make: Page 267 Predictions 7) ________ compute(s) the correlation of items on past orders to determine items frequently purchased together. Page 278 Market-Basket Analysis 8) If/then rules are used with which types of systems? Page 288 Expert systems. 9) Systems that ask if something is true and then give a response like, "If it is raining, then take an umbrella," are examples of which type of system? Page 288 Expert systems 10) Which of the following is NOT an example/characteristic of dirty data? Page 283 B for customer gender 213 for customer age. 00-9999-9999 for a phone number, a machine part colour of green and an email address of whyme@GuessWhoIam.com.au 11) On a web site, a customer's clickstream data provides a record of ________ on the site. Page 284 Everything done 12) ________ data is highly summarised. Page 284 a.fine b.Raw c.finised d.Coarse-건우형이 이거 골라서 햇는데 여기서 틀린것같음 ps. 빨강색 글씨는 틀려서 우리가 고쳐놓고 맞은거야 13) A data warehouse also...
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...International Journal For Technological Research In Engineering Volume 1, Issue 9, May-2014 ISSN (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...
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...mix, detect fraudulent activities and offer personalised promotion, different types of Business Intelligence Tools will be recommended specifically with the aim to achieve these priorities. 2 Assignment B Zoe Suet Yee Wan, Jason Lau, Yaoyu Su Table of Contents Executive Summary .................................................................................................... 2 1. Introduction .............................................................................................................. 4 2. Business Intelligence (BI) Tools ......................................................................... 5 2.1 Online Analytical Processing (OLAP) ............................................................ 5 2.2Data Mining...
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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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...leverage a data asset to achieve world-class supply chain efficiencies targeted primarily at driving down costs. Wal-Mart isn’t just the largest retailer in the world, over the past several years it has popped in and out of the top spot on the Fortune 500 list—meaning that the firm has had revenues greater than any firm in the United States. Wal-Mart is so big that in three months it sells more than a whole year’s worth of sales at number two U.S. retailer, Home Depot.[1] At that size, it’s clear that Wal-Mart’s key source of competitive advantage is scale. But firms don’t turn into giants overnight. Wal-Mart grew in large part by leveraging information systems to an extent never before seen in the retail industry. Technology tightly coordinates the Wal-Mart value chain from tip to tail, while these systems also deliver a mineable data asset that’s unmatched in U.S. retail. To get a sense of the firm’s overall efficiencies, at the end of the prior decade a McKinsey study found that Wal-Mart was responsible for some 12 percent of the productivity gains in the entire U.S. economy.[2] The firm’s capacity as a systems innovator is so respected that many senior Wal-Mart IT executives have been snatched up for top roles at Dell, HP, Amazon, and Microsoft. And lest one think that innovation is the province of only those located in the technology hubs of Silicon Valley, Boston, and Seattle, remember that Wal-Mart is headquartered in Bentonville, Arkansas. A Data-Driven Value...
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...Data Mining I found the topic of data mining very interesting in that it uncovers coveted information needed for improving and refining our daily lives. Information regarding traffic patterns, flight arrivals, consumer purchases, education, is collected and analyzed to improve a particular model. The data mining process is designed to gather information from a targeted sample which will enable companies to refine their business model in order to become more profitable. This process is not engineered to accumulate more information for an organization but to extract more meaningful information and correlate patterns of information that already exists in their data base. The importance of this information will allow companies to better analyze information to make quick effective decisions which will spur productivity. Data mining in turn can monitor and analyze these results to effectively manage assets. Organizations will be able to better predict the results of their decision making. How Data Mining Works A sample size is created by targeting large amounts of relevant information that is small enough to process. The information is then studied to find relationships which were anticipated , analyze trends, and recognize irregularities to gain knowledge for a design. “The data is then modified to transform the variables to focus the model selection process. A model is then selected by using analytical tools to search for a combination of data that reliably...
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...BUSINESS INTELLIGENCE RESEARCH BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords:...
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...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 to the higher education managements,...
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