MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The most commonly used measure of similarity is the Euclidean distance or its square. The Euclidean distance is the square root of the sum of the squared differences in values for each variable. Other distance measures are also available.

Detailed explanation-2: -Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions.

Detailed explanation-3: -Answer: The most common Measure of Similarity is Euclidean Distance. Explanation: Similarity measure in the context of data mining is the distance with dimensions representing features of the object.

Detailed explanation-4: -Euclidean distance measures the similarity of two things by calculating the distance between them. The chart below plots the companies based on their domestic and international markets exposures. In this case, the distance between Company A and C is 42.43, and between Company B and C is 56.57.

Detailed explanation-5: -Most clustering approaches use distance measures to assess the similarities or differences between a pair of objects, the most popular distance measures used are: 1. Euclidean Distance: Euclidean distance is considered the traditional metric for problems with geometry.