I’m trying to study for my Computer Science course and I need some help to understand this question.
Background: As noted by Kirk (2016), working with data is one of the four stages of the visualization workflow. According to Kirk (2016), “A dataset is a collection of data values upon which a visualization is based.” In this course, we will be using datasets that have already been collected for us. Data can be collected by various collection techniques.
Reference: Kirk, Andy. Data Visualisation: A Handbook for Data Driven Design (p. 50). SAGE Publications.
Assignment: Summarize 3 data collection techniques (Interviews, Surveys, Observations, Focus Groups, etc.). Compare and contrast the 3 data collection techniques you selected. Lastly, what collection techniques do you prefer and why?
Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
Data Mining “Anomaly Detection” Assignment
Answer the following questions. Please ensure to use the Author, YYYY APA citations with any content brought into the assignment.
- What are Anomalies/Outliers? And what are some variants of Anomaly/Outlier Detection Problems?
- What are some Challenges and Work Assumptions of Anomaly Detection?
- Explain the Nearest-Neighbor Based Approach and the different ways to Define Outliers.
- Explain the Density-based: LOF Approach.
- Provide the General Steps and Types of Anomaly Detection Schemes.
For assignment 2 need elobrted answers with no plagrism