Question

3- (2 point) [Data Stream Mining] Suppose the window of a data stream is as shown below. (Most recent bit is on the right side) ... 1 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 0 1 0 1 1 0 At least one of size 8 Two of size 4 One of size 2 Two of size 1 First, using the DGIM method, estimate the number of 1s in the last k positions, for the following values of k: \begin{itemize} \item k = 5 \item k = 16 \end{itemize} Second, for each value of k, what is the actual number of 1s (i.e., the exact number of 1s)? 4- (3 points) [Frequent Itemset Mining] A transaction database contains ten transactions as shown below: \begin{tabular}{|c|l|} \hline TID & Items bought \\ \hline 1 & \{s, u, s, a, n\} \\ 2 & \{s, a, r, a\} \\ 3 & \{s, a, m\} \\ 4 & \{r, i, c, h, a, r, d\} \\ 5 & \{e, r, i, c\} \\ 6 & \{n, i, c, k\} \\ 7 & \{p, a, t, r, i, c, k\} \\ 8 & \{e, m, i, l, y\} \\ 9 & \{c, h, a, r, l, e, s\} \\ 10 & \{l, i, d, a\} \\ \hline \end{tabular} And minimum_support = 30%. Answer the following questions: (a) Using A-priori, find all frequent itemsets that contain item i. Show the steps. (b) Calculate the confidence value of the following rules 1) \{$r, c\} \rightarrow \{i\} 2) \{$a, r\} \rightarrow \{i\} 3) \{$a\} \rightarrow \{i\} 4) \{$c\} \rightarrow \{i\} 5) \{$c\} \rightarrow \{$i, r\} 6) \{$r\} \rightarrow \{$i, c\} (c) Calculate the interest value for all the rules in (b).

          3- (2 point) [Data Stream Mining] Suppose the window of a data stream is as shown below.
(Most recent bit is on the right side)
... 1 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 0 1 0 1 1 0
At least one
of size 8
Two of size 4
One of
size 2
Two of
size 1
First, using the DGIM method, estimate the number of 1s in the last k positions, for the
following values of k:
\begin{itemize}
    \item k = 5
    \item k = 16
\end{itemize}
Second, for each value of k, what is the actual number of 1s (i.e., the exact number of 1s)?
4- (3 points) [Frequent Itemset Mining] A transaction database contains ten transactions
as shown below:
\begin{tabular}{|c|l|}
\hline
TID & Items bought \\
\hline
1 & \{s, u, s, a, n\} \\
2 & \{s, a, r, a\} \\
3 & \{s, a, m\} \\
4 & \{r, i, c, h, a, r, d\} \\
5 & \{e, r, i, c\} \\
6 & \{n, i, c, k\} \\
7 & \{p, a, t, r, i, c, k\} \\
8 & \{e, m, i, l, y\} \\
9 & \{c, h, a, r, l, e, s\} \\
10 & \{l, i, d, a\} \\
\hline
\end{tabular}
And minimum_support = 30%. Answer the following questions:
(a) Using A-priori, find all frequent itemsets that contain item i. Show the steps.
(b) Calculate the confidence value of the following rules
1) \{$r, c\} \rightarrow \{i\}
2) \{$a, r\} \rightarrow \{i\}
3) \{$a\} \rightarrow \{i\}
4) \{$c\} \rightarrow \{i\}
5) \{$c\} \rightarrow \{$i, r\}
6) \{$r\} \rightarrow \{$i, c\}
(c) Calculate the interest value for all the rules in (b).

3- (2 point) [Data Stream Mining] Suppose the window of a data stream is as shown below.
(Most recent bit is on the right side)
... 1 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 0 1 0 1 1 0
At least one
of size 8
Two of size 4
One of
size 2
Two of
size 1
First, using the DGIM method, estimate the number of 1s in the last k positions, for the
following values of k:

* k = 5

* k = 16

Second, for each value of k, what is the actual number of 1s (i.e., the exact number of 1s)?
4- (3 points) [Frequent Itemset Mining] A transaction database contains ten transactions
as shown below:

TID Items bought

1 {s, u, s, a, n}

2 {s, a, r, a}

3 {s, a, m}

4 {r, i, c, h, a, r, d}

5 {e, r, i, c}

6 {n, i, c, k}

7 {p, a, t, r, i, c, k}

8 {e, m, i, l, y}

9 {c, h, a, r, l, e, s}

10 {l, i, d, a}

And minimumsupport = 30%. Answer the following questions:
(a) Using A-priori, find all frequent itemsets that contain item i. Show the steps.
(b) Calculate the confidence value of the following rules
1) {r, c}→{i}
2) {a, r} →{i}
3) {a}→{i}
4) {c} →{i}
5) {c}→{i, r}
6) {r}→{i, c}
(c) Calculate the interest value for all the rules in (b).

Added by Samuel R.

Question

Please give Ace some feedback