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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 means algorithm. Choose the
points and as the initial centroids Find the coordinates of the
centroids after one iteration. Notice that the th coordinate of the centroid for
cluster is updated by
tilde
where symbol indicates the nearest integer to rounding Consider two objects and in a dimensional space where the data
including those is normally distributed with the variancecovariance matrix
Performing PCA using this data would produce and in the
dimensional principal components space, which are corresponding to and
respectively. Note that the length of and is meaning that the data
of principal component scores has the same dimension with the original data.
Let be the Mahalanobis distance between a and Which of the
following is true?
None the above. cannot compare. Each dataset shown below contains items and 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 ie itemsets must be contained in at least
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 are items. Suppose
that we set minsup
What is the total number of frequent itemsets that can be extracted?
Let and be the confidence values of the rules
and respectively. Assume that the rules have identical
support. Which of the following is true?
None of above
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