Dissimilarity

In data science, dissimilarity refers to the degree of difference or unlikeness between two objects or data points. It is a measure of how distinct or separate two objects are from each other based on their features or attributes. Dissimilarity can be calculated using various distance metrics such as Euclidean distance, Manhattan distance, and cosine distance. Dissimilarity plays a crucial role in clustering algorithms, where the goal is to group similar objects together and separate dissimilar ones. It is also used in classification tasks, where the dissimilarity between a new data point and existing ones is calculated to determine its class label.
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