Exercise docID document text docID document text 1 hot chocolate cocoa beans 7 sweet sugar 2345 9 cocoa ghana africa 8 sugar cane brazil beans harvest ghana 9 sweet sugar beet cocoa butter butter truffles sweet chocolate 10 sweet cake icing 11 cake black forest Clustering by k-means, with preprocessing tokenization, term weighting TFIDF. Manhattan Distance. Number of cluster is 2. Centroid docID 2 and docID 9.
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- 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 dataAssume using the agglomerative method to cluster the following numbers: 5, 13, 25, 40, 38, 45, 70. Use two distance methods of “single link” and “complete link” to perform clustering.Resolving collisions by using buckets that are linked chains is called. list resolution separate chaining bucket chaining O linked chaining
- Urfan 21Given numPQueue: 29, 38, 76 What does Peek(numPQueue) return? Ex: 100 After the following operations: Enqueue(numPQueue, 89) Enqueue(numPQueue, 84) Peek(numPQueue) Dequeue(numPQueue) What is numPQueue? Ex: 1, 2, 3 What does GetLength(numPQueue) return? Ex: 1 2 Next Check 1 3Algorithm to Relational k-Means ClusteringInput: T1, T4, number of clusters k, and number of iterations T
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