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[主观题]
Distance-based outlier Mining is not suitable to data set that does not fit any standard distribution model.
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发布时间：2022-01-07
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更多“Distance-based outlier Mining …”相关的问题
第2题
It is a blow for the Ugly Bettys and Plain Janes ——research shows that good looks lead to better pay. A study of 4,000 young men and women found that beauty boosted pay checks more than intelligence. Those judged to be the more attractive earned up to 10 percent more than their less attractive friends and colleagues. Applied to the average salary of ￡ 25,000 a year, the “ plainness penalty(处罚)”would make a difference of ￡2,500 a year —— or around ￡50 a week.
It is unclear what is behind the phenomenon but it may be that beauty creates confidence. The self-confident may appear to be doing better than they are and will not hesitate about asking for a pay rise.
Researcher Jason Fletcher, of Yale University in the U.S., rated the attractiveness of the 4,000 men and women. Just over half were judged average, while 7 percent were felt to be very attractive and 8 percent were judged unattractive or very unattractive. The volunteers also sat an IQ test and reported their salary. It became clear that pay scales were far from fair. For instance, a 14- point increase on the IQ score was associated with a 3 to 6 percent increase in wage. But being of above-average looks increased pay by 5 to 10 percent .
For a plain person to be paid the same as a very attractive one , they would have to be 40 percent brighter, the journal Economics Letters reports.
Dr Fletcher said:“The results do show that people ’s looks have an impact on their wages and it can be very important.”
6.What is the “plainness penalty ”？
A.To be paid less for being ordinarily-looking
B.To be laughed at for being ordinarily-looking
C.To be fired for being ordinarily-looking
D.To be questioned for being ordinarily-looking
How many people were rated as very attractive in Jason Fletcher ’s study？A.About 280
B.About 4,000
C.About 2,000
D.About 320
What ’s the average annual salary of a good-looking person？A.￡2,500
B.￡25,000
C.￡22,500
D.￡27,500
Which of the following best summarizes the main idea of the passage ？A.IQ is less important than appearance
B.Confidence makes people prettier
C.Good looks earn an extra penny
D.How Plain Janes get a higher salary
Good-looking people are usually paid more probably because________ .A.they look smarter
B.they have higher degrees
C.they are better at pleasing others
D.they are more confident
第3题
Jasmine doesn’t like the polo shirt because ________.
A、it doesn’t fit her well
B、she doesn’t like its color
C、it has shorts sleeves
D、it has a collar
第4题
What are the benefits of practising Yoga? 练习瑜伽的好处有些什么？
A、control weight
B、rebuild body
C、control anxiety
D、reduce stress
第5题
Statistic-based method needs to require knowing the distribution of the data and the distribution parameters in advance.
第6题
Mahalanobis Distance accounts for the relative dispersions and inherent correlations among vector elements, which is different from Euclidean Distance.
第7题
When identifying outliers with a discordancy test, the data point is considered as an outlier if it falls within the confidence interval.
第8题
Assignment 6 - Outlier mining You are required to use outlier mining methods to detect the outliers with given data sets. In a section of a city road, several cameras are set to collect the plate of vehicles from 2017-06-09 to 2017-06-12, as well as the date and time when passing the start point and the finish point. Travel time is calculated later. Time serial is another form of transformation from start time. So each instance contains 8 attributes, including serial number, license plate number, date and time passing start/end point, time serial and travel time. There are totally 4977 instances. You need to finish the following tasks. Task: (1) Use statistic-based approach to detect the outliers of travel time. Calculate the mean value and the variance of travel time. Write out the confidence interval. Take time serial as X-axis and the travel time as Y-axis. Plot the scatter diagram and mark the outliers you have recognized. (2) Use distance-based approach to detect the outliers of travel time. An object o in data set D is defined as an outlier with parameters r and π described as DB(r,π), if a fraction of the objects in D lie at a distance less than r from o is less than π, o is an outlier. Let parameter r vary from 0.1 to 0.3 with the step of 0.1, and π vary from 30 to 90 with the step of 30, find the outliers and the number of the outliers. You can use the Euclidian distance. (3) Use density-based approach to detect the outliers of travel time. With different k (from 3 to 400 with the step of 5), the number of neighbors, calculate the LOF for each data point. Set 2.0 as a threshold for LOF and an object is labeled as an outlier if its LOF exceeds 2.0. Firstly, take k value as X-axis and the number of outliers as Y-axis. Plot the line chart. Secondly, calculate the LOF for each data point and give the top 4 outliers. Use k=350 and the Euclidian distance.
