University CS 450 Data Mining‚ Fall 2014 Take-Home Test N#1 Date: September 22nd‚ 2014 Final deadline for submission September 29th‚ 2014 Weighting: 5% Total number of points: 100 Instructions: 1. Attempt all questions. 2. This is an individual test. No collaboration is permitted for assessment items. All submitted materials must be a result of your own work. Part I Question 1 [20 points] Discuss whether or not each of the following activities is a data mining task.
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Data Mining Melody McIntosh Dr. Janet Durgin Information Systems for Decision Making December 8‚ 2013 Introduction Data mining‚ or knowledge discovery‚ is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends‚ allowing businesses to make proactive‚ knowledge- driven decisions Although data mining is still in its infancy
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Data Mining On Medical Domain Smita Malik‚ Karishma Naik‚ Archa Ghodge‚ Shivani Gaunker Shree Rayeshwar Institute of Engineering & Information Technology Shiroda‚ Goa‚ India. Smilemalik777@gmail.com; naikkarishma39@gmail.com; archaghodge@gmail.com; shivanigaunker@gmail.com Abstract-The successful application of data mining in highly visible fields like retail‚ marketing & e-business have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries
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Module 815 Data Structures Using C M. Campbell © 1993 Deakin University Module 815 Data Structures Using C Aim After working through this module you should be able to create and use new and complex data types within C programs. Learning objectives After working through this module you should be able to: 1. Manipulate character strings in C programs. 2. Declare and manipulate single and multi-dimensional arrays of the C data types. 3. Create‚ manipulate and manage C pointers
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Sample Midterm I Econ 3790: Statistics for Business and Economics Instructor: Yogesh Uppal You are allowed to use a standard size (8.5*11) cheat sheet and a simple calculator. Please write all the answers with a BALL-POINT PEN or an INK PEN. If you have any questions during the exam‚ please raise your hand. GOOD LUCK!!! I am sure you guys will do great. Multiple Choice Identify the letter of the choice that best completes the statement or answers the question and write it in the space given
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Big data describes innovative methods and technologies to capture‚ distribute‚ manage and analyze larger-sized data sets with high rate and diverse structures that conventional data management methods are unable to handle. Digital data is now everywhere—in every sector public or private‚ economy‚ organization and customer of digital technology. There are many ways that big data can be used to create value across sectors of the global economy. It has demonstrated the capacity to improve predictions
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Growing Pigs without Affecting Growth Performance1«2 CHRISTINA M. EVOCK-CLOVER‚3 MARILYN M. POLANSKY‚* RICHARD A. ANDERSON* AND NORMAN C. STEELE Nonruminant Animal Nutrition Laboratory and *Vitamin and Mineral Nutrition Laboratory‚ USDA-Agricultural Research Service‚ Beltsuille‚ MD 20705 increase in insulin internalization in rat muscle cells (Evans and Bowman 1992) with a concomitant in crease in glucose and leucine uptake. Chromium chloride and chromium nicotinate had no effect. Chromium supplementation
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1 First two lectures about big data. So why this surge? Isn’t about data at all Systems being able to process data Tools exploit and derive valuable nuggets NOT NEW: Walmart Wallstreet decades Why not out? Competition Teradata 20 years‚ contest MR patent Digital exhaust incr Know your Stats‚ gaga tweet sentiment analysis Correlation causation 2 Old: Centralized systems (data came from humans) Sun hardware Oracle software Moore’s law -> data grew. Data exhausts growing larger
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LECTURE 1 DATA TYPES Our interactions (inputs and outputs) of a program are treated in many languages as a stream of bytes. These bytes represent data that can be interpreted as representing values that we understand. Additionally‚ within a program we process this data that can be interpreted as representing values that we understand. Additionally‚ within a program we process this data in various way such as adding them up or sorting them. This data comes in different forms. Examples include: your
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