An itemset X is called a generator on a data set D if there does not...
An itemset X is called a generator on a data set D if there does not exist a proper sub-itemset Y ⊂ X such that support(X) = support(Y). A generator X is a frequent generator if support(X) passes the minimum support threshold. Let G be the set of all frequent generators on a data set D. (a) Can you determine whether an itemset A is frequent and the support of A, if it is frequent, using only G and the support counts of all frequent generators? If yes, present your algorithm. Otherwise, what other information is needed? Can you give an algorithm assuming the information needed is available? b. what is the relationship between closed itemsets and generators?
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An itemset X is called a generator on a data set D if there does
not...
Table 1: Data set of market-basket transactions ansaction ID Items Bought [A, B, D, E (B, C, D (A...
Table 1: Data set of market-basket transactions ansaction ID Items Bought [A, B, D, E (B, C, D (A, B, D, E) A, C, D, E) (B,C, D, E B, D, E (C, D) (A, B, C (A, D, E) 6 7 [15 points] Answer the following questions for the data set in Table 1. (a) What is the maximum number of association rules that can be extracted from this data set (including rules that have zero support)? (b) What...
Consider the transactional database shown in the following table. Transaction ID Items Bought T100 Plum, Apple,...
Consider the transactional database shown in the following table. Transaction ID Items Bought T100 Plum, Apple, Peach, Orange, Pear, Banana T200 Cherry, Apple, Peach, Orange, Pear, Banana T300 Plum, Mango, Orange, Pear, Kiwi, Strawberry T400 Plum, Watermelon, Avocado, Orange, Banana T500 Avocado, Apple, Orange, Lemon, Pear CONDITION: The minimum support is 60% and minimum confidence is 70%. Based on the CONDITION above, answer the following five questions. (1) Find all frequent itemsets using the Apriori algorithm. Show how the algorithm...
For the data set shown below, complete parts (a) through (d) below. x 20 30 40...
For the data set shown below, complete parts (a) through (d) below. x 20 30 40 50 60 y 98 95 89 83 70 (a) Use technology to find the estimates of Bo and B. Bobo = (Round to two decimal places as needed.) B1b1 = (Round to two decimal places as needed.) (b) Use technology to compute the standard error, the point estimate for Se = (Round to four decimal places as needed.) (c) Assuming the residuals are normally...
For the data set shown below, complete parts (a) through (d) below x 20 30 40...
For the data set shown below, complete parts (a) through (d) below x 20 30 40 50 60e yi 98 95 93 83 70 (a) Use technology to find the estimates of Po and β1 Po b,-□ (Round to two decimal places as needed.) β1 ~ b1-D (Round to two decimal places as needed) (b) Use technology to compute the standard error, the point estimate for σ. Round to four decimal places as needed) (c) Assuming the residuals are normally...
For the data set shown below, complete parts (a) through (d) below X 20 30 40...
For the data set shown below, complete parts (a) through (d) below X 20 30 40 y 98 95 91 50 60 85 68 (a) Use technology to find the estimates of Bo and B Pobo (Round to two decimal places as needed.) (Round to two decimal places as needed.)
For the data set shown below, complete parts (a) through (d) below. x y 20 98...
For the data set shown below, complete parts (a) through (d) below. x y 20 98 30 95 40 89 50 85 60 72 (a) Use technology to find the estimates of Β0 and Β1 . Β0 ≈ b0 =__?__ (Round to two decimal places as needed.) Β1 ≈ b1 =__?__ (Round to two decimal places as needed.)
For the data set shown below, complete parts (a) through (d) below. x 3 4 5...
For the data set shown below, complete parts (a) through (d) below. x 3 4 5 7 8 y 3 7 6 11 14 (a) Find the estimates of beta 0 and beta 1 . beta 0 almost equalsb 0equalsnothing (Round to three decimal places as needed.) beta 1 almost equalsb 1equalsnothing (Round to three decimal places as needed.)
For the data set shown below, complete parts (a) through (d) below. x 2020 3030 4040...
For the data set shown below, complete parts (a) through (d) below. x 2020 3030 4040 5050 6060 y 9898 9595 8989 8383 6868 (a) Use technology to find the estimates of beta 0β0 and beta 1β1. beta 0β0almost equals≈b 0b0equals=nothing (Round to two decimal places as needed.) beta 1β1almost equals≈b 1b1equals=nothing (Round to two decimal places as needed.)
A data set is given below. (a) Draw a scatter diagram. Comment on the type of...
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x overbarxequals=3.83333.8333, s Subscript xsxequals=2.40142.4014, y overbaryequals=4.15004.1500, s Subscript ysyequals=1.51891.5189, and requals=negative 0.9349−0.9349, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). x 00 22 44 55 66 66 y 5.85.8 5.95.9 4.74.7 3.23.2 2.52.5 2.82.8 (a) Choose the correct graph below....
Really short question! The second picture is some information about the data set. Please help me...
Really short question! The second picture is some information
about the data set. Please help me to solve, thank you!
(30%)Q4 (EM algorithm): The heights of n 8000 students are drawn from a school. Assume the height largely depends on the gender. We denote the height of student i by Yi, and the gender of student i by Z..(Y; : 1 i n) are observed, but {Zi : 1 i n} are unknown Our model can be formulated as follows....
Table 1: Data set of market-basket transactions ansaction ID Items Bought [A, B, D, E (B, C, D (A, B, D, E) A, C, D, E) (B,C, D, E B, D, E (C, D) (A, B, C (A, D, E) 6 7 [15 points] Answer the following questions for the data set in Table 1. (a) What is the maximum number of association rules that can be extracted from this data set (including rules that have zero support)? (b) What...
For the data set shown below, complete parts (a) through (d) below. x 20 30 40 50 60 y 98 95 89 83 70 (a) Use technology to find the estimates of Bo and B. Bobo = (Round to two decimal places as needed.) B1b1 = (Round to two decimal places as needed.) (b) Use technology to compute the standard error, the point estimate for Se = (Round to four decimal places as needed.) (c) Assuming the residuals are normally...
For the data set shown below, complete parts (a) through (d) below x 20 30 40 50 60e yi 98 95 93 83 70 (a) Use technology to find the estimates of Po and β1 Po b,-□ (Round to two decimal places as needed.) β1 ~ b1-D (Round to two decimal places as needed) (b) Use technology to compute the standard error, the point estimate for σ. Round to four decimal places as needed) (c) Assuming the residuals are normally...
For the data set shown below, complete parts (a) through (d) below X 20 30 40 y 98 95 91 50 60 85 68 (a) Use technology to find the estimates of Bo and B Pobo (Round to two decimal places as needed.) (Round to two decimal places as needed.)
Really short question! The second picture is some information
about the data set. Please help me to solve, thank you!
(30%)Q4 (EM algorithm): The heights of n 8000 students are drawn from a school. Assume the height largely depends on the gender. We denote the height of student i by Yi, and the gender of student i by Z..(Y; : 1 i n) are observed, but {Zi : 1 i n} are unknown Our model can be formulated as follows....
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