...Introducción Walt Disney conocido por crear una de las marcas más reconocidas del mundo, desarrolló la idea de crear un lugar donde toda la familia pudiera divertirse, "We believe in our idea: a family park where parents and children could have fun — together."1. Tras una inversión de 17 millones de dólares, ese sueño se hizo realidad el 17 de julio de 19552 al inaugurar el primer parque de atracciones de Disney en California. Disney siempre tuvo una obsesión con los detalles y entendió que el Core del negocio era sorprender a sus visitantes, decidió enfocar toda su estrategia empresarial en comprender a los clientes y observar cómo actúan promoviendo así, una cultura centrada en el cliente3. Cada momento que vive un cliente en el parque con cualquiera de los empleados, las atracciones o locaciones hace parte de la experiencia y por ello es importante cuidar el impacto que puede generar en sus usuarios y así tomar decisiones basadas en ello y no en beneficios exclusivos para la compañía. Walt Disney World es un generador de experiencias memorables, la huella que deja en la vida de cada uno de sus visitantes es tan eficaz que son ellos quienes no paran de promocionar este destino. Así se evidencia que enfocar los esfuerzos en transformar la visita del cliente y cultivar relaciones permitirá una viralización positiva de la compañía, convirtiéndolos en el voz a voz más poderoso y creando el deseo en los turistas de volver al parque. Según el ranking anual elaborado...
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...CHAPTER ONE INTRODUCTION 1.1Background to the study Mining is the extraction of minerals and precious metals from the earth. These minerals and metals consist of manganese, tantalum, copper, tin, silver, diamonds and gold. Mining may be considered in two forms: large scale mining and small scale mining. Large scale mining generally employs large number of people and produces huge tonnes of gold. Examples of companies who engage in these are the Anglo-Gold Ashanti of Ghana, Newmont Ghana, Goldfields Ghana and Minas Serra Palade Mines in Brazil which employed about over thousands workers and yielded thousands tonnes of gold (Amankwah and Anim-Sackey, 2003). Small scale mining is a form of mining that is done at small levels and mostly employs relatively a low number of people (Appiah, 1998). It is generally engaged in by local people within the area where these activities occur, and comes along with it the influx of people from other areas. Small Scale Mining companies use a considerable number of the labour force in the country. While there is no accurate SSM employment number for Ghana (Appiah, 1998), it is estimated that some 500,000 people are openly employed in the sector while additional 500,000 may indirectly be benefiting from the doings. About half of those directly engaged in the S.S.M are said to be illegal operators (Amankwah & Anim-Sackey, 2003) commonly known as “galamsey operators”. The actions of small-scale miners also generate economic linkages with other...
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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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...Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? Mieke Jans, Nadine Lybaert, Koen Vanhoof Abstract Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study. 1 Introduction Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim...
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...UNIVERSITY FOR DEVELOPMENT STUDIES,GHANA CENTER FOR CONTINUING EDUCATION AND INTERDISCIPLINARY RESEARCH (CCEIR), NAVRONGO A PROPOSAL BY MUMUNI ELIASU For A MASTER OF ART DEGREE IN ENVIRONMENTAL SECURITY AND LIVELIHOOD CHANGE INTRODUCTION 1.2 Background Since the beginning of civilization of mankind, man found the use of minerals as indispensable material in sustaining life with the “lion cave” being the oldest known mine from Swaziland which proved to be about 43,000 years old. Hungary and the Ancient Egypt also mined flint and malachite respectively for weaponry tools and ornaments. (http://en.wikipedia.org/wiki/Mining) Mining generally is the extraction of valuable mineral deposits or other geological materials from the ground or earth. These deposits could be gold, bauxite, manganese, precious metals, diamond, oil, coal, limestone and many others. Any material that cannot be grown through agricultural processes created artificially in a laboratory or factory is normally mined. Mining normally involves prospecting for the mineral and final exploration if found, in the form of surface (strip) or underground mining. Surface mining is when the soil and rocks overlying the mineral deposits are removed. It is used when deposits of commercially useful minerals or rocks are found near the surface; that is, where the overburden(surface material covering the valuable deposit) is relatively thin or the material of interest is structurally unsuitable...
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...Data Mining for Predictive Analytics Stanley Kenton Marks December 11th, 2012 Abstract Simply collecting data for research is nearly a faux pas in today’s competitive web-market. Analysts are now looking toward the predictive analytics of association discovery in web and data mining, to find Business Intelligence of clustering sub=populations while eliminating errors to keep collected data valid. In the midst this data crunch are fears of lost privacy. Do not fear. Creative innovations are bringing mash-ups to our diversity. Data Analytics Report Useful information, knowledge and finding some unexpected results can “strike it rich” with added creative thinking. Data mining supplies analysts, investors, and traders with customers buying patterns, historical trading rules, even fraudulent behavior for insurance claims. Predictive analytics is used in web mining by analyzing user’s movements from one web content to another. Collecting the data of where a user browses and the content they are seeking can become knowledge if the analyst understands the patterns (Turban & Volonino, 2011). An Association Discovery Algorithm is a tool of data mining where new rules are discovered such that if one item is present then another will also be found. This type of knowledge benefits analyst’s predictability of future probabilities and is very useful to the marketing department, (Ranjan, 2008). A traditional example you...
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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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...tree algorithms A. B. Adeyemo (Ph.D)1 and S. O. Kuyoro (M.Sc.)2 Department of Computer Science, University of Ibadan, Ibadan, Nigeria Abstract The causes of the difference in the academic performance of students in tertiary institutions has for a long time been the focus of study among higher education managers, parents, government and researchers. The cause of this differential can be due to intellective, non-intellective factors or both. 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,...
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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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...identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analysed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5. Keywords: Naïve Bayes, Multilayer Perceptron, C4.5, medical data mining, medical decision...
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...com/locate/techsoc Data mining techniques for customer relationship management Chris Rygielski a, Jyun-Cheng Wang b, David C. Yen a,∗ a Department of DSC & MIS, Miami University, Oxford, OH, USA b Department of Information Management, National Chung-Cheng University, Taiwan, ROC Abstract Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-ori- ented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the...
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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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...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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...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 that, each Board of studies has adhered to the resolutions passed by Faculty of Technology, and developed curriculum accordingly. In addition to outcome based education, semester based credit and grading system is also introduced to ensure quality of engineering education. Semester based Credit and Grading system enables a much-required shift in focus from teacher-centric to learner-centric...
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...www.pwc.co.uk The direct economic impact of gold October 2013 www.pwc.co.uk The work carried out by PricewaterhouseCoopers LLP ("PwC") in relation to this report has been carried out only for the World Gold Council and solely for the purpose and on the terms agreed between PwC and the World Gold Council. The report does not constitute professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this report and, to the extent permitted by law, PricewaterhouseCoopers LLP, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences to anyone acting, or refraining to act, in reliance on the information contained in this report or for any decision based on it. © 2013 PricewaterhouseCoopers LLP. All rights reserved. In this document, "PwC" refers to PricewaterhouseCoopers LLP (a limited liability partnership in the United Kingdom), which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. The direct economic impact of gold Contents Foreword ........................................................................................................................................................................1 Executive summary ...........................................................................................................................................
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