#CosineDistance #BigDataAnalytics Cosine Distance (Big Data Analytics)
Channel: Tech Educators
Date: 2020-12-04

In this video you will, what is Cosine Distance? Why we use cosine distance?
How to find the similarity between the two documents using cosine distance?
Cosine similarity is used to determine the similarity between documents or vectors. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. 
The relation between cosine similarity and cosine distance can be define as below:
Similarity decreases when distance between two vectors increases
Similarity increases when distance between two vectors decreases.
Cosine distance between two data points(vectors) P1 & P2 considering two axis X and Y.
Case 1: When angle between points P1 & P2 is 45 Degree then
cosine_similarity= Cos 45 = 0.525
Case 2: When two points P1 & P2 are far from each other and angle between points is 90 Degree then
cosine_similarity= Cos 90 = 0
Case 3: When two points P1 & P2 are very near and lies on same axis to each other and angle between points is 0 Degree then
cosine_similarity= Cos 0 = 1
Case 4: When points P1 & P2 lies opposite two each other and and angle between points is 180 Degree then
cosine_similarity= Cos 180 = -1
Case 5: When angle between points P1 & P2 is 270 Degree then
cosine_similarity= Cos 270 = 0
Case 6: When angle between points P1 & P2 is 360 Degree then
cosine_similarity= Cos 360 = 1
#Cosine Distance
#cosine Similarity
#proximity measures
#Distance Measure
#Big Data Analytics
#Document Similarity
#Document Dissimilarity


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