Therefore, the smaller the distance is, the larger the similarity will get.Ī given distance(e.g. In other terms, A and B have a strong correlation. Thus, the similarity between A and B is higher than A and C or B and C. As you can tell, A and B are close enough to each other in contrast to C. Each data sample can have a single value on one axis(because we only have one input feature) let’s denote that as the x-axis. This can be considered the simplest example to show the dissimilarity between three data points A, B, and C. Let’s take an example where each data point contains only one input feature. One means high similarity(the data objects are very similar). It is often expressed as a number between zero and one by conversion: zero means low similarity(the data objects are dissimilar). The similarity measure is usually expressed as a numerical value: It gets higher when the data samples are more alike. Another example is when we talk about dissimilar outliers compared to other data samples(e.g., anomaly detection). KNN), where the data objects are labeled based on the features’ similarity. All other data samples are grouped into different ones. Moreover, these terms are often used in clustering when similar data samples are grouped into one cluster. On the other hand, the dissimilarity measure is to tell how much the data objects are distinct. In data science, the similarity measure is a way of measuring how data samples are related or closed to each other. Illustrations and equations were generated using tools like Matplotlib, Tex, Scipy, Numpy and edited using GIMP. Quick note: Everything written and visualized has been created by the author unless it was specified. “There is no Royal Road to Geometry.” - Euclid
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