What is clustering, define k-means clustering, its algorithm, explain complexity and why it always converges.

please code in pythonApply a KMeans model to decouple the anomalous data from the normal data. Fill in the myKMeans function which accepts as input the training set and returns a fully trained clustering model.Template:def myKMeans(Xtrain, ytrain):    #write function here         return myKMeansYou can use any sample data

Algorithm to Relational k-Means ClusteringInput: T1, T4, number of clusters k, and number of iterations T

Hello. Please answer the attached Hashing in Data Structures question correctly and follow all directions. Please give the solution for the problem. Do not copy the answers already given on Chegg or Bartleby. I just want your own personal answer. *If you solve the problem by yourself and it is correct I will provide a thumbs up for you. Thank you.

In this hypothetical case, you have a database with the x, y coordinates and names of all nearby restaurants. The following enquiries are required: If the location is suitable for a restaurant, the investigation examines. Why is B-tree indexing better than R-tree? Why?

What are the disadvantages of using K - Means clustering (select all that apply)?   Group of answer choices a. Choosing k value manually b. It is often slow c. It highly depends on Initial values d. Clustering outliers

Let M(m,n) denote the number of comparisons required to merge two sorted lists of sizes m and n.   a) Derive an "information theoretic" lower bound on M(m,n).   b) Find an interesting relation between M(m,n) and the analogous function S defined for sorting. Use this relation to derive a lower bound for M(m,n) in terms of S. (Hint: Start with an upper bound on S(m+n).

please, I need to solve these, it contain python code and results.

[TRUE / FALSE] K-means clustering algorithms can find clusters of arbitrary shape. [TRUE / FALSE] The silhouette coefficient is a method to determine the natural number of clusters for partitioning algorithms. [TRUE / FALSE] DBSCAN clustering algorithm optimize an objective function. [TRUE / FALSE] A common weakness of association rule mining is that it produces too many frequent itemsets. [TRUE / FALSE] Lift corrects for high confidence in rule X-> Y when item X is bought regularly by customers. [TRUE / FALSE] A low support of X will increase the confidence of X -> Y. [TRUE / FALSE] When we have X -> Y and Y -> X then support of X is equal to support of Y. [TRUE / FALSE] In general, agglomerative clustering is slower than quadratic. [TRUE / FALSE] The best centroid for minimizing the SSE of a cluster is the mean of the points in the cluster. [TRUE / FALSE] Apriori principle indicates if an itemset is infrequent, then all of it subsets must also be infrequent. [TRUE /…

Subject: Design Analysis and Algorithm Please write handwritten Answer AndExplain Briefly Please don't use ChatGPT