Problem 2. It is important to dene or select similarity measures in data analysis. However, there is no commonly- accepted subjective similarity measure. Results can vary depending on the similarity measures used. Nonetheless, seemingly different similarity measures may be equivalent after some transformation. Suppose we have the following two-dimensional data set: (a) Consider the data as two-dimensional data points. Given a new data point, a = (1.4,l.6) as a query, rank the database points based on similarity with the query using Euclidean distance, Manhattan distance, supremum distance, and cosine similarity. (b) Use two ways (min-max and zscore) to normalize the data set to make the norm of each data point equal to I. Use Euclidean distance on the transformed data to rank the data points