What are the centroids of the following set of values if we cluster them using 2 means algorithm? Show every cluster created before the final cluster is reached. (19, 7, 16, 3, 18, 10, 21}
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What are the centroids of the following set of values if we cluster them using 2 means
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- K-means clustering is run on some data, for a few different values of K. Shown is the plot obtained for the inertia (a measure of average distance of points from the centroid of their allocated cluster), versus the number of clusters. What is the implied natural number of clusters to use? Inertia O 2000 1750 1500 1250 1000 750 500 250 T 9 3 1 -2 3 4 5 6 Number of clusters 7 .8 9We are going to use K-means algorithm to cluster 6 data points from dataset D = {0, 1, 2, 3, 4, 2022} in R1 into 2 clusters. Before the first iteration, the cluster centers are randomly initialized at c1 = 0.235 and c2 = 1.984. Next, we simulate the first iteration (for part (a) and (b)) of K-means with manual computation. (a) Compute the cluster assignment for each of the 6 data points given, using the Euclidean distance. [5 pts] (b) Compute the updated cluster center c1 and c2. [5 pts] (c) How many iterations are needed to finish the K-means algorithm for this problem? [10 pts]Correct answer will be upvoted else downvoted. Computer science. You are given a cluster a comprising of n integers. At first all components of an are either 0 or 1. You wanted to deal with q inquiries of two sorts: 1 x : Assign to cut out the worth 1−ax. 2 k : Print the k-th biggest worth of the cluster. As an update, k-th biggest worth of the cluster b is characterized as following: Sort the cluster in the non-expanding request, return k-th component from it. For instance, the second biggest component in exhibit [0,1,0,1] is 1, as in the wake of arranging in non-expanding request it becomes [1,1,0,0], and the second component in this cluster is equivalent to 1. Input The principal line contains two integers n and q (1≤n,q≤105) — the length of the given cluster and the number of questions. The subsequent line contains n integers a1,a2,a3,… ,an (0≤ai≤1) — components of the underlying cluster. Every one of the accompanying q lines contains two integers. The…
- Given a set of three points, -2, 0, and 9, we want to use k- Means Clustering with k = 2 to cluster them into two clusters. If the initial cluster centers are C1 = -4.0 and C2 = 2.0, What will be the center of C1 after completing first iteration of k means clustering algorithm? Answer:Cluster the following eight points with (x, y). Representing locations into three clusters. Initial clusters are: A2 ,A4, A6. The distance function between two points a=(x1, y1) and b=(x2, y2) if defined as : P (a, b)=|x2-x1|+|y2-y1|. Use K-means algorithm to find the three clusters centers after the second iteration? A1 5.1 3.5 1.4 0.2 A2 4.9 3 1.4 0.2 A3 7 3.2 4.7 1.4 A4 6.4 3.2 4.5 1.5 A5 6.3 3.3 6 2.5 A6 5.8 2.7 5.1 1.9The procedure of evaluating the results of a clustering algorithm is known under the termcluster validity. In general terms, there are two approaches to investigate cluster validity Internal and External criteria. Both DB (Davies-Bouldin) and Silhouette Coefficient are internal criteria. Which one is NOT correct about these two criteria? Group of answer choices The minimum DB score is zero, with lower values indicating better clustering. DB is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The best value of Silhouette is 1 and the worst value is -1 No answer text provided.
- Assume, you want to cluster 8 observations into 3 clusters using K-Means clustering algorithm. After the first iteration clusters C1, C2, C3 have the following observations: C1: {(2,3), (4,3), (6,6)} C2: {(0,4), (4,0)} C3: {(5,5),(7,7), (9,9)} What will be the Euclidean distance for observation (9, 9) from cluster centroid C1 in the second iteration?Suppose that you need to group some data into clusters, and you run K-means 100 times, starting each time from a different random initialisation. You get 100 different clusterings of the data. Which one should you choose? Simply pick one at random. Choose the one that minimises the mean distance of points from the centroid of their allocated cluster. Label the clusters 1, 2, ..., Choose the clustering you get from the 100th run. k each time, and assign each point to the cluster it ends up in most often.Consider the following distance table (matrix) for six objects. Draw DENDROGRAMs by using the hierarchical clustering with single and complete links techniques. Also, find the number of cluster distance d=0.26. A B с D E F B A 0 0.12 0 0.51 0.25 0.84 0.16 0.28 0.77 0.34 0.61 C D E F 0 0.14 0 0.70 0.70 0.45 0 0.93 0.20 0.67 0
- Correct answer will be upvoted else downvoted. Computer science. You are given an exhibit a1,a2,… ,a comprising of n positive integers and a positive integer m. You should separate components of this cluster into certain exhibits. You can arrange the components in the new clusters as you need. How about we call a cluster m-distinct if for every two adjoining numbers in the exhibit (two numbers on the positions I and i+1 are called neighboring for every I) their aggregate is separable by m. A variety of one component is m-separable. Track down the most modest number of m-distinct exhibits that a1,a2,… ,an is feasible to separate into. Input The main line contains a solitary integer t (1≤t≤1000) — the number of experiments. The principal line of each experiment contains two integers n, m (1≤n≤105,1≤m≤105). The second line of each experiment contains n integers a1,a2,… ,an (1≤ai≤109). It is ensured that the amount of n and the amount of m over all experiments…Suppose there are six cities in a state. The distance matrix between each pair of the cities is given below. What is the dendrogram generated by hierarchical clustering with single-linkage? A В D E F A 60 120 185 260 300 60 90 210 258 320 120 185 90 140 179 200 210 132 140 179 125 E 260 258 125 135 300 320 200 132 135 Based on the resulting dendrogram, if we want to create 4 clusters, they are Cluster Number Items 1Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. Andre has quite certain preferences. As of late he began becoming hopelessly enamored with clusters. Andre calls a nonempty cluster b great, if amount of its components is distinguishable by the length of this exhibit. For instance, cluster [2,3,1] is acceptable, as amount of its components — 6 — is distinguishable by 3, yet exhibit [1,1,2,3] isn't acceptable, as 7 isn't separable by 4. Andre considers a cluster an of length n great if the accompanying conditions hold: Each nonempty subarray of this cluster is acceptable. For each I (1≤i≤n), 1≤ai≤100. Given a positive integer n, output any ideal cluster of length n. We can show that for the given limitations such a cluster consistently exists. A cluster c is a subarray of an exhibit d if c can be gotten from d by cancellation of a few (conceivably, zero or all) components from the start and a few (perhaps, zero or…