IndexSemantic similaritySimilarity measuresEuclidean distanceManhattan distanceCosine similarityJaccard coefficientSemantic similaritySemantic similarity is a metric defined on a set of documents or terms, where the idea of ​​distance between them is based on the similarity of the their meaning or semantic content as opposed to the similarity that can be estimated regarding their syntactic representation (e.g. their format of string). These are mathematical tools used to estimate the strength of the semantic relationship between linguistic units, concepts or instances, through a numerical description obtained based on the comparison of information that supports its meaning or describes its nature. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Similarity is subjective and highly dependent on the domain and application. For example, two fruits are similar in color, size or taste. Care must be taken when calculating the distance between unrelated dimensions/features. The relative values ​​of each element must be normalized, otherwise one feature may end up dominating the distance calculation. Similarities are measured in the range 0 to 1 [0,1].Similarity MeasuresA similarity measure is the measure of how similar two given objects are. The similarity measure in the context of data mining is a distance between points of dimensions that represent characteristics of objects. If this distance is small, it will be the high degree of similarity while a large distance will be the low degree of similarity. A similarity measure is also known as a similarity function, a real-valued function that quantifies the similarity between two objects. Although there is no single definition of a similarity measure, usually such measures are in some sense the inverse of distance metrics: they take large values ​​for similar objects and zero or a negative value for very dissimilar objects. Similarity between two documents or Documents Vs Query Terms: A similarity measure can be used to calculate the similarity between two documents, two queries, or a document and a query. Document Classification: The similarity measure score can be used to classify documents. All clustering algorithms use similarity or so-called "distance" functions to determine cluster members. Some of the most popular similarity measures are discussed in the following subsections. Euclidean distance It is a standard metric for geometric problems. It is ordinary distance between two points and can be easily measured with a ruler in two- or three-dimensional space. Euclidean distance is widely used in clustering problems, including the default distance measure used with the algorithm K-means. Distance measurement between text documents: given two documents, da and db represented by their term vectors ta and tb respectively. The Euclidean distance of the two documents is defined as:Where, the set term is T = {t1 , t2,..….., tn}In this calculation Wt,a = tf-idf(da,t)Euclidean distance is the most common use of distance In most cases, when talking about distance, Yes will refer to the Euclidean distance. The Euclidean distance is also simply called distance. When the data is dense or continuous, this is the best measure of proximity. Manhattan Distance Manhattan Distance is a metric where the distance between two points is the sum of the absolute differences of their Cartesian coordinates. In a simple way to say it is the sum total of the difference between the x coordinates and the y coordinates. Suppose we have two points A and B