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Data MiningFor the following vectors, \mathbf{x} and \mathbf{y}, calculate the indicated similarity or distance measures : (i) \mathbf{x} = (0, -1, 0, 1), \mathbf{y} = (1, 0, -1, 0) cosine, correlation (ii) \mathbf{x} = (0, 1, 0, 1), \mathbf{y} = (1, 0, 1, 0) Euclidean, SMC20227mData MiningFor the following vectors, x and y, calculate the indicated similarity or distance measures : \mathbf{x} = \{0, 1, 0, 1\}, \quad \mathbf{y} = \{1, 0, 1, 0\} Calculate: 1. Cosine similarity 2. Correlation 3. Euclidean distance 4. Jaccard similarity20214mMACHINE LEARNINGWhich of the following is an example of supervised learning? (i) K-means clustering (ii) Linear regression (iii) PCA (iv) DBSCAN20252mMACHINE LEARNINGDiscuss the steps in K-means algorithm and evaluate the following table using K-means : 202214m
PreviousA company wants to predict the sales (y) of a product based on its advertising expenditure (x). The observed data for 5 months is: | x (Advertising) | y (Sales) | |-----------------|-----------| | 1 | 3 | | 2 | 4 | | 3 | 2 | | 4 | 5 | | 5 | 7 | Calculate the regression coefficients for the linear regression model: y = w_0 + w_1 x Then, predict the value of y when x = 6.NextExplain the concept of linear classifiers. How do they separate classes in feature space? Give examples of linearly separable and non-separable datasets.