Task 1: Weka Visualization and Analysis For this task, you are required to install the Weka software, that you will then use throughout the duration of this subject. You will also do some tasks with the Weka software for data visualization and analysis. The first task will build the practical and technical skills that will enable you to compare and evaluate output patterns for visualization. Load the cpu.with.vendor.arff dataset as follows: Click on the ‘Open file’ button on the top left corner of the Weka panel, select the data folder and then the file named cpu.with.vendor.arff. Use this data set to answer the following questions: How many instances and attributes does the dataset have? [1 mark] List the attributes of the dataset [1 mark] List the values of the vendor attribute. How are they different from values of the MYCT attribute? [2 marks] What is the class value of instance number 12 of the dataset? [1 mark] Task 2: Written Exercise Topic: Privacy, Addictive and Manipulative behavior in Social Media In this task, you are required to read the journal articles provided below and write a short discussion paper based on the topic of how data mining techniques are used in social media applications such as Facebook and Youtube to compromise privacy and encourage addictive and manipulative behavior. You must: explain how data mining is used to compromise privacy and encourage addictive and manipulative behavior; evaluate the significance of these effects for individuals and society as a whole; discuss the ethical implications of these data mining practices; and support your response with appropriate examples and references. The task is worth 10 marks of the overall marks available for assessment 2. The recommended word length for this posting is 1000 to 1500 words. Journal articles: https://www.channel4.com/news/jaron-lanier-interview-on-how-social-media-ruins-your-life https://www.nytimes.com/2018/04/11/technology/facebook-privacy-hearings.html https://medium.com/@IAMEIdentity/the-facebook-data-mining-scandal-what-happened-82154855aeca Rationale back to top This assessment task will assess the following learning outcome/s: be able to identify and analyse business requirements for the identification of patterns and trends in data sets. be able to appraise the different approaches and categories of data mining problems. be able to compare and evaluate output patterns. be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements. be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set. be able to identify and evaluate the security, privacy and ethical implications in data mining. Marking criteria and standards back to top Task 1: Weka Visualization & Analysis The grade you receive for this assessment as a whole is determined by the cumulative marks gained for each question. The tasks in this assessment involve a sequence of several steps and therefore you will be marked on the correctness of your answer as well as clear and neat presentation of your diagrams, where required. Task 2 - Written Exercise Criteria HD DI CR PS Demonstrate an ability to analyse, reason and discuss the concepts learned in the subject (This includes content from online meetings, textbook chapters, modules, readings and forum discussions) Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are logically supported by examples and best practice. Answers succinctly integrate and link information into cohesive and coherent piece of analysis and consistently use correct data mining terminologies and sophisticated language. Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are logically supported by examples and best practice. The answers are logically structured to create cohesive and coherent piece of analysis that consistently use correct data mining terminologies. Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are generally logically structured to create a comprehensive, mainly descriptive piece of analysis. Some use of correct data mining terminologies. Demonstrate an ability to analyse, reason and discuss most concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are partially structured into loosely-linked rudimentary sentences to create a comprehensive, descriptive piece of analysis. Some use of correct data mining terminologies. Presentation back to top Assignments are required to be submitted in either Word format (.doc, or .docx), Open Office format (.odf), Rich Text File format (.rtf) or .pdf format. All diagrams that are required should be inserted into the document in the appropriate position.