Assume that we use cosine similarity as the similarity measure. In the hierarchical agglomerative clustering (HAC), we need to define a good way to measure the similarity of two clusters. One usual way is to use the group average similarity betweendocuments in two clusters. Formally, for two cluster C; and C, let C= C;U Cj, n = C, we define1sim(C,, C) =E s(x, y)Σn.(n- 1) x.y € C, x =yWhere s(x,is the cosine similarity betweeny)andy.Given a list of clusters C. C2, .... Cm assume that their pairwise similarities are saved in a two dimensional array**",of sizem2. Given three clusters C, C;, and Cr show that there is a way to compute sim(C;U C;, C) in constant time. Note that we ignore the dimensionality in time complexity.

Hierarchical agglomerative clustering HAC is Bottom-up hierarchical clustering, which is frequently…

Assume that we use cosine similarity as the similarity measure. In the hierarchical agglomerative clustering (HAC), we need to define a good way to measure the similarity of two clusters. One usual way is to use the group average similarity between documents in two clusters. Formally, for two cluster C, and C, let C=C,U C,, n = |C|, we define sim(C, C) =G-D'E s(x, v) n (n-1 x.y € C, x*y Where s(x. v) is the cosine similarity between x and y. Given a list of clusters C. Ca. . Coy assume that their pairwise similarities are saved in a two dimensional array of size m2. Given three clusters C. C., and Cu show that there is a way to compute sim(C.UC. CL) in constant time. Note that we ignore the dimensionality in time complexity.

Assume that we use cosine similarity as the similarity measure. In the hierarchical agglomerative clustering (HAC), we need to define a good way to measure the similarity of two clusters. One usual way is to use the group average similarity between documents in two clusters. Formally, for two cluster C, and C, let C=C,U C,, n = |C|, we define sim(C, C) =G-D'E s(x, v) n (n-1 x.y € C, x*y Where s(x. v) is the cosine similarity between x and y. Given a list of clusters C. Ca. . Coy assume that their pairwise similarities are saved in a two dimensional array of size m2. Given three clusters C. C., and Cu show that there is a way to compute sim(C.UC. CL) in constant time. Note that we ignore the dimensionality in time complexity.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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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 1
Correct answer will be upvoted else downvoted. Computer science. Officially, you should find a cluster b1,b2,… ,bn, to such an extent that the arrangements of components of exhibits an and b are equivalent (it is identical to cluster b can be found as a cluster a with some reordering of its components) and ∑i=1nMEX(b1,b2,… ,bi) is expanded. MEX of a bunch of nonnegative integers is the negligible nonnegative integer to such an extent that it isn't in the set. For instance, MEX({1,2,3})=0, MEX({0,1,2,4,5})=3. Input The primary line contains a solitary integer t (1≤t≤100) — the number of experiments. The primary line of each experiment contains a solitary integer n (1≤n≤100). The second line of each experiment contains n integers a1,a2,… ,an (0≤ai≤100). Output For each experiment print an exhibit b1,b2,… ,bn — the ideal reordering of a1,a2,… ,an, so the amount of MEX on its prefixes is amplified. If there exist different ideal answers you can view as any
Suppose we have three cluster centroids µl=[1, 2], µ2=[-3, 0] and µ3=[4,2]. Furthermore, we have a training example x(i)=[-1,2]. After a cluster assignment step, which cluster will x(i) belong to - c(1), c(2) or c(3)?
Develop a K-Mean Clustering program (using python) and classify the following data into 3 clusters. Individual 1, 3, 4 are the centroids of the initialize partitions. The clustering result should be output (e.g. using print function). Students must develop their own K-Mean Clustering program (K-means built function in any library should not be used.) Individual Variable 1 Variable 2 1 1.0 1.0 2 1.5 2.0 3 3.0 4.0 4 5.0 7.0 3.5 5.0 6. 4.5 5.0 7 3.5 4.5
The 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.
Note: c++ language code Error code eill downvoted. Chanek has a cluster an of n integers. The attractiveness worth of an is meant as: ∑i=1n∑j=1ngcd(ai,aj)⋅gcd(i,j) where gcd(x,y) signifies the best normal divisor (GCD) of integers x and y. At the end of the day, the beauty worth of a cluster an is the all out amount of gcd(ai,aj)⋅gcd(i,j) for all sets (i,j). Help Mr. Chanek discover the attractiveness worth of a, and output the outcome modulo 109+7! Input :the principal line contains an integer n (2≤n≤105). The subsequent line contains n integers a1,a2,… ,an (1≤ai≤105). Output :Output an integer signifying the beauty worth of a modulo 109+7.
Design an adjacency Matrix of the alphabets of your full name. In accordance with the following conditions: Let my name is Muhammad Adnan If your name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If your Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.
Design an adjacency Matrix of the alphabets of Uzair Bhatti . In accordance with the following conditions: If your name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If your Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix
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 9
Design an adjacency Matrix of the alphabets of my name (Isha Tir Razia). In accordance with the following conditions: If name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.
Traditional clustering methods are rigid in that they require each object to belong exclusively to only one cluster. Explain why this is a special case of fuzzy clustering. You may use k-means as an example.
Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. You are given an exhibit [a1,a2,… ,an]. You will probably track down the length of the longest subarray of this cluster with the end goal that the most continuous worth in it isn't interesting. At the end of the day, you are searching for a subarray with the end goal that assuming the most successive worth happens f times in this subarray, something like 2 distinct qualities ought to happen precisely f times. An exhibit c is a subarray of a cluster d if c can be gotten from d by erasure of a few (potentially, zero or all) components from the start and a few (conceivably, zero or all) components from the end. Input The primary line contains a solitary integer n (1≤n≤200000) — the length of the exhibit. The subsequent line contains n integers a1,a2,… ,an (1≤ai≤min(n,100)) — components of the cluster. Output You should output precisely one integer — the length of the…