It is important to define 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 2-D data set:A1 A2
x1 1.5 1.7
x2 2 1.9
x3 1.6 1.8
x4 1.2 1.5
x5 1.5 1.0
(a) Consider the data as 2-D data points. Given a new data point, x = .1.4, 1.6/ as a
query, rank the database points based on similarity with the query using Euclidean
distance, Manhattan distance, supremum distance, and cosine similarity.
(b) Normalize the data set to make the normof each data point equal to 1. Use Euclidean
distance on the transformed data to rank the data points.

Which of the following “invisible” marks represents an inserted tab?

You are new to microsoft certification and want to start out by getting a certification geared around windows 8. what microsoft certification should you pursue?

Jason, samantha, ravi, sheila, and ankit are preparing for an upcoming marathon. each day of the week, they run a certain number of miles and write them into a notebook. at the end of the week, they would like to know the number of miles run each day, the total miles for the week, and average miles run each day. write a program to them analyze their data. your program must contain parallel arrays: an array to store the names of the runners and a two-dimensional array of five rows and seven columns to store the number of miles run by each runner each day. furthermore, your program must contain at least the following functions: a function to read and store the runners’ names and the numbers of miles run each day; a function to find the total miles run by each runner and the average number of miles run each day; and a function to output the results. (you may assume that the input data is stored in a file and each line of data is in the following form: runnername milesday1 milesday2 milesday3 milesday4 milesday5 milesday6 milesday7.)