Part 1- 250 opinion
Create a argument course (with your indicate) and counter-argument the aftercited inquiry:Argument (Chapter 4): What are the secrecy issues with facts mining? Do you fancy they are substantiated?
Your confutation should be 250-300 opinion.
Part 2- 200 opinion
Replies to 2 students each 100 opinion
part 3 – APA monograph 300 opinion
Chapter 4 – argument inquiry #1-5 & exertion 1
Examine how odd facts stop devices such as RFID tags succor cems correspondently authenticate and part their customers coercion activities such as targeted marketing. Multifarious of these applications mingle facts mining. Scan the attainment and the Web and then propound five possible odd facts mining applications that can authentication the facts created with RFID technology. What issues could originate if a country’s laws required such devices to be embedded in everyone’s assemblage coercion a referableorious identification order?
2. Interview administrators in your discernd-plot or executives in your cem to mention how facts mining, facts warehousing, Online Analytics Ordering (OLAP), and visualization tools could succor them in their exertion. Write a proposition describing your findings. Include consume estimates and benefits in your fame. 3. A very amiable recipient of facts that has been authenticationd to touchstindividual the execution of multifarious facts mining algorithms is beneficial at ics.uci.edu/~mlearn/MLRepository.html. Some of the facts cemals are meant to touchstindividual the limits of popular machine-tuition algorithms and to collate their execution with odd approaches to tuition. However, some of the smaller facts cemals can be authenticationful coercion exploring the functionality of any facts mining software, such as RapidMiner or KNIME. Download at last individual facts cemal from this recipient (e.g., Credit Screening Factsbases, Housing Factsbase) and devote conclusion tree or clustering methods, as alienate. Prepare a fame inveterate on your results. (Some of these exertions, chiefly the individuals that mingle liberal/challenging facts/problem may be authenticationd as semester-long promise projects.)
4. Liberal and feature-wealthy facts cemals are made beneficial by the U.S. legislation or its subsidiaries on the Internet. Coercion prompting, discern a liberal assemblage of legislation facts cemals (data.gov), the Centers coercion Disease Control and Prevention facts cemals (www.cdc.gov/DataStatistics), Surveillance, Cancer.org’s Epidemiology and End Results facts cemals (http://seer.cancer.gov/data), and the Department of Transportation’s Fatality Analysis Fameing Order jar facts cemals (www.nhtsa.gov/FARS). These facts cemals are referable preprocessed coercion facts mining, which makes them a sublime productions to proof the thorough facts mining order. Another wealthy beginning coercion a assemblage of analytics facts cemals is listed on KDnuggets.com (KDnuggets.com/datasets/index.html).
1. Visit the AI Exploratorium at cs.ualberta.ca/~aixplore. Click the Conclusion Tree integrate. Read the truth on basketball pastime statistics. Examine the facts, and then found a conclusion tree. Fame your impressions of its hit. Also discuss the property of divergent algorithms
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