# Homework 2  (Advanced) Data Mining: Algorithms and Applications

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Homework 2
(Advanced) Data Mining: Algorithms and Applications
1. Find the distance between objects 1 and 3 by using the formula provided on the slides. Notice that we have mixed type
of attributes. (You can scan and submit your handwritten calculation) (25/20 points)
Object Identifier test-1(nominal) test-2 (ordinal) test-3 (numeric)
1 A excellent 45
2 B fair 22
3 C good 64
4 A excellent 28
2. Write a program in any language which can compute Manhattan and Euclidean distances between any two given vectors with any length. You can pass the length to your function, but please don’t limit the dimension to 2. You can test
3. In the table below, determine whether passing a class has a dependency on attendance by using Chi-square test.
Please refer to the formula in the slides. (25/20 points)
(For the expected value for each cell, multiply the total counts in the rows and columns of the cell and divide by total
count.
For example: Expected value for Attended-Pass=33*31/54 = 18.94. You can scan and submit your handwritten calculation)
Passed Failed Total
Attended 25 6 31
Skipped 8 15 23
Total 33 21 54
4. In R, there is a built-in data frame called mtcars. Please calculate the correlation between mpg and wt attributes of
mtcars by using cor() function. Then generate scatter plot based on these two attributes. Your scatter plot should be
like the one below. You don’t need to submit the image, but R script should be submitted (25/20 points)
5. Grad Students Only Write an R or Python script which removes or drops the columns which have more than 75%
missing values. Then it should replace the missing values in the remaining columns with the median value of the
1
existing values of that particular column. Download metabolite.csv from Google Drive and use this data set to test your
code. Please check the end of this document for some useful R examples and hints. (10 points)
6. Grad Students Only Please apply PCA on the processed metabolites data and create a scatter plot by using first two
principal components in which points are colored based on the Label column. Please submit your code along with
your figure in the same file. (10 points)
(If you are going to use R, you may need to use which(), is.na() functions and consider excluding those columns by name. For
that purpose you may investigate %in% and -c(…) type of operations. You can also see examples of subsetting a dataframe
below with their outputs. It’s also recommended to check tidyverse library.) (10 points)
# A sample data frame
subject sex size
1 M 7
2 F 6
3 F 9
4 M 11
‘)
subset(data, subject < 3)
#> subject sex size
#> 1 1 M 7
#> 2 2 F 6
data[data\$subject < 3, ]
#> subject sex size
#> 1 1 M 7
#> 2 2 F 6
# Subset of particular rows and columns
subset(data, subject < 3, select = -subject)
#> sex size
#> 1 M 7
#> 2 F 6
subset(data, subject < 3, select = c(sex,size))
#> sex size
#> 1 M 7
#> 2 F 6
subset(data, subject < 3, select = sex:size)
#> sex size
#> 1 M 7
#> 2 F 6
data[data\$subject < 3, c(“sex”,”size”)]
#> sex size
#> 1 M 7
#> 2 F 6
# Logical AND of two conditions
subset(data, subject < 3 & sex==”M”)
#> subject sex size
#> 1 1 M 7
data[data\$subject < 3 & data\$sex==”M”, ]
#> subject sex size
#> 1 1 M 7
# Logical OR of two conditions
subset(data, subject < 3 | sex==”M”)
2
#> subject sex size
#> 1 1 M 7
#> 2 2 F 6
#> 4 4 M 11
data[data\$subject < 3 | data\$sex==”M”, ]
#> subject sex size
#> 1 1 M 7
#> 2 2 F 6
#> 4 4 M 11
# Condition based on transformed data
subset(data, log2(size) > 3 )
#> subject sex size
#> 3 3 F 9
#> 4 4 M 11
data[log2(data\$size) > 3, ]
#> subject sex size
#> 3 3 F 9
#> 4 4 M 11
# Subset if elements are in another vector
subset(data, subject %in% c(1,3))
#> subject sex size
#> 1 1 M 7
#> 3 3 F 9
data[data\$subject %in% c(1,3), ]
#> subject sex size
#> 1 1 M 7
#> 3 3 F 9
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