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    data mining

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    Introduction to Data Mining Assignment 1 Ex1.1 what is data mining? (a) Is it another hype? Data mining is Knowledge extraction from data this need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. So‚ data mining definitely is not another hype it can be viewed as the result of the natural evolution of information technology. (b) Is it a simple transformation of technology developed from databases

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    Secondary Data

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    1 Secondary data analysis: an introduction All data are the consequence of one person asking questions of someone else. (Jacob 1984: 43) This chapter introduces the field of secondary data analysis. It begins by considering what it is that we mean by secondary data analysis‚ before describing the type of data that might lend itself to secondary analysis and the ways in which the approach has developed as a research tool in social and educational research. The second part of the chapter considers

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    Data Sonification

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    Turnage‚ Bonebright‚ Buhman‚ Flowers (1996) showed that untrained participants can listen to shapes. That is‚ they used data sonification – musical representation of two dimensional space‚ with pitch as the vertical dimension and time as the horizontal dimension – to present participants the visual and auditory representation of waveforms. In two conditions‚ they showed the participants could match one visual presentation to one of two auditory representations‚ or match one auditory presentation

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    Data Mining

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    Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta By Sumayya Iqbal SP09-BSB-036 Zainab Khan SP09-BSB-045 BS Thesis (Feb 2009-Jan 2013) COMSATS Institute of Information Technology Islamabad- Pakistan January‚ 2013 COMSATS Institute of Information Technology Data Mining And Statistical Approaches In Identifying Contrasting Trends In Reactome And Biocarta A Thesis Presented to COMSATS Institute of Information Technology‚ Islamabad In

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    Data Mining

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    SMS CUSAT Reading Material on Data Mining Anas AP & Alex Titty John • What is Data? Data is a collection of facts and information or unprocessed information. Example: Student names‚ Addresses‚ Phone Numbers etc. • What is a Database? A structured set of data held in a computer which is accessible in various ways. Example: Electronic Address Book‚ Phone Book. • What is a Data Warehouse? The electronic storage of large amount of data by business. Concept originated in

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    Data Processing

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    1. Data Processing- is any process that a computer program does to enter data and‚ summarize‚ analyze or otherwise convert data into usable information. The process may be automated and run on a computer. It involves recording‚ analyzing‚ sorting‚ summarizing‚ calculating‚ disseminating and storing data. Because data are most useful when well-presented and actually informative‚ data-processing systems are often referred to as information systems. Nevertheless‚ the terms are roughly synonymous‚ performing

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    data communications

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    robustness‚ stability‚ fairness‚ optimality‚ and efficiency. 12.5 For fixed routing‚ a single‚ permanent route is configured for each sourcedestination pair of nodes in the network. 12.6 With flooding‚ a packet is forwarded to all other switches so that eventually all routes between source and destination are traversed. 12.7 Advantages: (1) An adaptive routing strategy can improve performance‚ as seen by the network user. (2) An adaptive routing strategy can aid in congestion control. Because

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    Data Types

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    Dynamic Dependency Analysis of Ordinary Programs 1 Todd M. Austin and Gurindar S. Sohi Computer Sciences Department University of Wisconsin-Madison 1210 W. Dayton Street Madison‚ WI 53706 faustin sohig@cs.wisc.edu A quantitative analysis of program execution is essential to the computer architecture design process. With the current trend in architecture of enhancing the performance of uniprocessors by exploiting ne-grain parallelism‚ rst-order metrics of program execution‚ such as operation frequencies

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    Big DATA

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    4V of Big Data? Imagine all the information you alone generate each time you swipe your credit card‚ post to social media‚ drive your car‚ leave a voicemail‚ or visit a doctor. Now try to imagine your data combined with the data of all humans‚ corporations‚ and organizations in the world! From healthcare to social media‚ from business to the auto industry‚ humans are now creating more data than ever before. volume‚ velocity‚ variety‚ and veracity. Volume: Scale of Data Big data is big. It’s

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    Data Warehousing

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    and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach unique to data warehousing. Rather than building

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