Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

Similarity Measures Utility Package

Project description

Determining similarity or distance between two objects is a key step for several data mining and knowledge discovery tasks. For quantitative data, Minkowski distance plays a major role in finding the distance between two entities. The prevalently known and used similarity measures are Manhattan distance which is the Minkowski distance of order 1 and the Euclidean distance which is the Minkowski distance of order 2. But, in the case of categorical data, we know that there does not exist an innate order and that makes it problematic to find the distance between two categorical points. This is a utility package for finding similarity measures such as Eskin, IOF, OF, Overlap (Simple Matching), Goodall1, Goodall2, Goodall3, Goodall4, Lin, Lin1, Morlini_Zani (S2), Variable Entropy and Variable Mutability. These similarity measures help in finding the distance between two or more objects or entities containing categorical data.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for Categorical-similarity-measures, version 0.4
Filename, size File type Python version Upload date Hashes
Filename, size Categorical_similarity_measures-0.4.tar.gz (4.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page