Anyone who is broadly familiar with analytical techniques email list will find that there are many algorithms that depend on the distance between the data points of your application. Each observation or data instance is usually represented as a multidimensional vector, and the input to the algorithm requires the distance between each pair of such observations. The method of calculating the distance depends on the type of data (number, category, or mixture). Some algorithms apply to observations in only one class, while others work in multiple classes. This post describes distance measures that process numerical data. There are probably more ways to measure distance in a multidimensional hyperspace than can be covered by a single blog post, and you can always devise new ways, but general distance metrics and their relative benefits. Examine some of them. For the rest of the purpose of the blog post, we mean refers to two observations or data vectors. First, prepare the email list data ... Before we can see the various distance metrics, we need to prepare the data. Conversion to a numeric vector for mixed observations that include both numerical and categorical dimensions, the first step is actually to convert the categorical dimensions to numerical dimensions. Category dimensions with three potential values ​​can email list be converted to two or three numeric dimensions with binary values. This categorical variable inevitably takes one of three values, so one of the three numerical dimensions correlates perfectly with the other two. This may or may not be a problem for some applications. If the observations are purely categorical, such as text strings (sentences of various lengths) or genomic sequences (fixed-length sequences), special distance metrics can be applied directly without converting the data to numerical format. Increase. These algorithms will be discussed in the next post. Normalization in