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Using Manhattan distance, we will apply the k - means algorithm. Choose the points D and H as the initial centroids ( k = 2

Using Manhattan distance, we will apply the k-means algorithm. Choose the
points D and H as the initial centroids (k=2). Find the coordinates of the
centroids after one iteration. Notice that the h th coordinate of the centroid for
cluster i is updated by
tilde(x)h(i)=[1|Ci|ainCi?xah]
, where 'y' symbol indicates the nearest integer to y(rounding y). Consider two objects xi and xj in a p-dimensional space where the data
including those is normally distributed with the variance-covariance matrix .
Performing PCA using this data would produce yi and yj in the p-
dimensional principal components space, which are corresponding to xi and
xj respectively. Note that the length of yi and yj is p, meaning that the data
of principal component scores has the same dimension with the original data.
Let MD(a,b) be the Mahalanobis distance between a and b. Which of the
following is true?
(1)MD(xi,xj)>MD(yi,yj)
(2)MD(xi,xj)=MD(yi,yj)MD(xi,xj)
(3)MD(xi,xj)=MD(yi,yj)
(4) None of the above. We cannot compare. Each dataset shown below contains 1,000 items and 10,000 transactions. Dark
cells indicate ones (presence of items) and white cells indicate zeros (absence of
items). We will apply the apriori algorithm to extract frequent itemsets with
minsup =10%(i.e., itemsets must be contained in at least 1,000
transactions.). Which of the following correctly lists the dataset producing the
fewest number of frequent itemsets and the dataset producing the frequent
itemset with highest support? Sequentially type two alphabet letters. Consider a transactional database where 1,2,3,4,5,6,7 are items. Suppose
that we set minsup =0.5.
What is the total number of frequent 2-itemsets that can be extracted?
(1)3
(2)4
(3)5
(4)6Let c1,c2, and c3 be the confidence values of the rules {a}{b},{a}
{b,c}, and {a,c}{b} respectively. Assume that the rules have identical
support. Which of the following is true?
(1)c1c2
(2)c1c3
(3)c2c3
(4) None of above
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