SURVEY PAPER Top 10 algorithms in data mining Xindong Wu · Vipin Kumar · J. Ross Quinlan · Joydeep Ghosh · Qiang Yang · Hiroshi Motoda · Geoffrey J. McLachlan · Angus Ng · Bing Liu · Philip S. Yu · Zhi-Hua Zhou · Michael Steinbach · David J. Hand · Dan Steinberg Received: 9 July 2007 / Revised: 28 September 2007 / Accepted: 8 October 2007 Published online: 4 December 2007 © Springer-Verlag London Limited 2007 Abstract This paper presents the top 10 data mining algorithms identified by the IEEE
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types of programming languages that combine imperative control structures (for example‚ for loops‚ while loops and if-then-else statements) with statements of the data manipulation language.These languages‚ sometimes called fourth-generation languages‚ often include special features to facilitate the generation of forms and the display of data on the screen. Most major commercial database systems include a fourthgeneration language. Sophisticated users
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Professional Studies (MProfStuds) - Data Science. Since the day I graduated from my college‚ I always thought that with time‚ while attaining some professional experience‚ I would unveil the subject I want to be prowess. Now when I am sitting on a professional experience of more than 2 years‚ I realised I am somehow always working on the quantitative data in order to come out with a qualitative research. With a moderate amount of knowledge attained in data management in my academics‚ I embarked
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Introduction Hauling represents 50% percent (Kennedy‚1990) of the operating costs in a shovel - truck open pit mining operation‚ efforts to control or reduce hauling cost in order to reduce their impact over the mining cost has been done along the time. The most common approach is to improve the efficiency of the hauling fleet‚ reducing idle and waiting times for the trucks by assign them to a loading equipment in real time‚ this can be done using ad hoc software as Dispatch® or Jigsaw® . These
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organizations require to manage themselves efficiently and effectively. The future trends in MIS are strategic importance for businesses. Businesses use information systems at all levels of operation to collect‚ process and store data. Management aggregates and disseminates this data in the form of information needed to carry out the daily operations of business. Everyone who works in business‚ from someone who pays the bills to the person who makes employment decisions‚ uses information systems. A car
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The dimension of a dataset‚ which is the number of variables‚ must be reduced for the data mining algorithms to operate efficiently. We present and discuss several dimension reduction approaches: (1) Incorporating domain knowledge to remove or combine categories‚ (2) using data summaries to detect information overlap between variables (and remove or combine redundant variables or categories)‚ (3) using data conversion techniques such as converting categorical variables into numerical variables
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Gartner Research in 1989 [7]. Howard Dresner of Gartner Research‚ who is also widely recognised as the father of BI‚ first coined the term as “a broad category of software and solutions for gathering‚ consolidating‚ analysing and providing access to data in a way that lets enterprise users make better business decisions” [7]. However‚ this research found that the term business intelligence was used as early as 1958 by Luhn in an IBM journal
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Innovations to ‘Active Enterprise Intelligence’ such that it will be much easier to integrate multiple organizations’ data warehouses and provide cross-organization‚ collaborative data warehousing. Examples of cross-organization applications include data mining and reporting on the efficacy of business processes shared with suppliers‚ distributors‚ customers and government agencies. The global market was facing recession‚ a crucial transition phase where all businesses need strong efforts to overcome
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and respond to the consumer by analyzing this unsolicited feedback. Given the volume‚ format and content of the data‚ the appropriate approach to understand this data is to use large-scale web and text data mining technologies. This paper argues that applications for mining large volumes of textual data for marketing intelligence should provide two key elements: a suite of powerful mining and visualization technologies and an interactive analysis environment which allows for rapid generation and testing
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MA9219 2 CS9211 3 CS9212 4 SE9213 5 CS9213 PRACTICAL 6 CS 9215 7 CS9216 COURSE TITLE L T P C Operations Research Computer Architecture Data Structures and Algorithms Object Oriented Software Engineering Computer Networks and Management 3 3 3 3 3 1 0 0 0 0 0 0 0 0 0 4 3 3 3 3 Data Structures Lab Networking Lab 0 0 15 0 0 1 3 3 6 2 2 20 TOTAL LIST OF ELECTIVES FOR M.E.COMPUTER SCIENCE AND ENGINEERING* SL. NO 1
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