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Q: c. S1[1] => d. S1[1:3] => e. s1 + s2[-1] =>
A: s1+s2=> [2, 1, 4, 3, 'c', 'a', 'b'] 2. 3*s1+2.s2==> [2, 1, 4, 3, 2, 1, 4, 3, 2, 1, 4, 3,…
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A: Option B is the correct answer
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Q: syms t 241 60 sin(161) e x=60*sin(16*t)*exp(-((24*t)/5))% Displacement v=diff(x)% Velocity -241 960…
A: code description syms t syms function creates a symbolic object that is automatically assigned to…
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A: Solution :
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A: tmp->next is equal to p2->next
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A: Solution :
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A: C++ Code and output of the above question is in step 2.
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- Consider the dataset: {0, 4, 5, 20, 25, 39, 43, 44} Suppose we want the two top level clusters from this dataset. What will single link and complete link output as the two clusterings?Suppose that the data mining task is to cluster points (with (x,y) representing location coordinates) into three clusters, where the points are A1(2,10), A2(8,4), A3(7,5), A4(1,2), A5 (2,5), A6(5,8), A7(6,4), A8(4,9). The distance function is Euclidean distance and k-means (with k=3) for clustering. Suppose initially we assign A1, A4, and A6 as the centroid for the three clusters. Show the three cluster centroids as well as a set of points in each cluster after each round until convergence.Following is the table of pairwise Euclidean distances between 5 data points a b c d e a 0 b 9 0 c 3 7 0 d 6 5 9 0 e 11 10 2 8 0 Assuming we are using single-linkage ( min distance ) for hierarchical clustering. The order in which the clusters will be formed is: Group of answer choices a. Points b and d are merged first to form cluster {b, d}.Then point a is incorporated into cluster {b, d}. Then points c and e are merged into {c, e}. Finally, clusters {b, d, a} and {c, e} are merged. b. Points c and e are merged first to form cluster {c, e}.Then point b is incorporated into cluster {c, e}. Then points a and d are merged into {a, d}. Finally, clusters {c, e, b} and {a, d} are merged. c. Points c and e are merged first to form cluster {c, e}.Then point a is incorporated into cluster {c, e}. Then points b and d are merged into {b, d}. Finally, clusters {c, e, a} and {b, d} are merged. d. Points b and d are merged first to form…
- Consider the following data points on X-Y plane and implement the complete linkage hierarchical clustering over the following data points. data points = {(1,1),(2,1),(4,1),(2,6),(3,3),(4,4),(5,3)} Complete linkage distance from cluster A and B can be calculated using the below formula: d(A,B) = max x∈A,x ′∈B δ(x, x ′ ) where δ(x, x ′ ) should be taken as Manhattan distance (e.g., δ((1,2),(3,1)) = |1−3|+|2−1| = 3). (Note: Show your calculation at each iteration to get the full credit.) 1. Implement the complete linkage hierarchical clustering over above data points. (Show the steps!) 2. Draw the dendrogram after obtaining the clusters from complete linkage based hierarchical clustering. 3. Can hierarchical clustering help in detecting outliers? Why/How? 4. In general, can we prune the dendrogram? How?Question # 2. Clustering CLO3,4, PLO-1,2,3, C-1,3 16 Let’s suppose, there are seven data points {A, B, C, D, E, F and G} which lie on different locations. Table 2 describes the distance between given points which can be read horizontally or vertically. Table 2. A B C D E F G A 0 365 381 220 1325 808 673 B 365 0 225 575 995 730 320 C 381 225 0 590 970 948 415 D 220 575 590 0 1540 890 890 E 1325 995 970 1540 0 1462 710 F 808 730 948 890 1462 0 810 G 673 320 415 890 710 810 0 Use complete link agglomerative clustering to group the data points: Show the dendogram. Also tell the number of clusters at distance 300. Perform the k-mean clustering. Suppose that the initial seeds (centers of each cluster) are A, D and G. Run the k-means algorithm for 1 epoch only. At the end of epoch show the new clusters.One-dimensional data set is determined as follows {1,2, 6, 7, 8. 15, 17, 20}. Please useK-Means Clustering algorithm to cluster the above data into three clusters; withinitial centroid C1 = 2, and C2 = 10, and C3 = 17. Please use the Euclidean Distance formulato calculate the distance between each data with its centroid.
- Calculate the equilibrium PageRank values of the four nodes below. You can explain your reasoning in words or mathematically. Your reasoning can be either analytical or intuitive. No initialpage rank values are given in the assignment.In the DBSCAN clustering algorithm, consider three border points A, B, and C. The numbers of other points within Eps distance from A, B, and C are 7, 2, and 5, respectively. What is the minimum possible value for the minpoints parameter of the DBSCAN algorithm?Following is the normalized distance matrix of sales data for four customers of a company. Apply single linkage clustering until only one option remains. Draw a dendogram illustrating the clustering process. What conclusions can you draw from this analysis for this company? Customer 1 2 3 4 1 0 3.88382 2.08990 2.70886 2 0 3.21080 1.97620 3 0 2.99488 4 0
- Consider the dataset: {0, 4, 5, 20, 25, 39, 43, 44} Which one is a better solution: group the dataset into two clusters or three clusters? Why?code to describe how to create the links dataset. For the network projection, the nodes partitions must also beprovided. The nodes dataset with the partition flag is also created. The projected network is based on the subset of nodes A,B, C, D, E, and F. This subset of nodes is set in the nodes dataset with the partition id 1. The nodes projection will be throughthe common neighbors in the subset of nodes G, H, I, and J. This subset is flagged with partition id 0.Q3: With the aid of a diagram, show the sequence of query/response messages that are exchanged to carry out a recursive name-to-address resolution. Include in your diagram a root name server, a top-level name server, and lower-level name servers. Use a DNS of the following URL: http://www.coeng.uobaghdad.edu.iq