Giter Site home page Giter Site logo

n-serrette / cluster_index Goto Github PK

View Code? Open in Web Editor NEW
1.0 2.0 0.0 1.04 MB

Implementation of some intern and extern clustering indexes

License: GNU General Public License v3.0

Python 100.00%
clustering-indexes clustering clustering-evaluation clustering-coefficient jaccard measure

cluster_index's Introduction

Cluster_Index

Build Status tests Coverage Status Maintainability

Several clustering indexes implementation

External Indices

This indexes are designed to measure the similarity of two partitions.

Indexes based on pair counting

All this indices are based on counting pair depending on wether they belong to the same cluster or not according to the partition C or C'. There are four possibilities:

  • yy : the two points belong to the same cluster according to both C and C'
  • yn : the two points belong to the same cluster according to C but not to C'
  • ny : the two points belong to the same cluster according to C' but not to C
  • nn : the two points does not belong to the same cluster according to C and C'

Precision

precision

Recall

recall

Rand Index [3]

ri

Adjusted Rand Index

ari

Folkes-Mallows Index [4]

folkesMallows

Jaccard Index

jaccard

Kulczynski Index

kulczynski

McNemar Index

McNemar

Phi Index

phi

Rogers and Tanimoto Index

rogerstanimoto

Russel and Rao Index

russelRao

Solkal and Sneath Index (version 1 and 2)

Hubert Index

Mirkin Metric

Measures based on set overlaps

F-measure

Also call Czebanowski-Dice index or Ochiai index

fmeasure

Measures based on Mutual Information

Mutual Information

Strehl and Ghosh Normalized Mutual Information

Fred and Jain Normalized Mutual Information

Variation of Infomation

Other

Purity

Entropy

References

  1. Silke Wagner and Dorothea Wagner, Comparing Clustering - An Overview, 2007
  2. Bernard Desgraupes, Clustering Indices, 2016, [https://CRAN.R-project.org/package=clusterCrit]
  3. Rand, William M.: Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association, 66(336):846-850, 1971.
  4. Fowlkes, E. B., Mallows, C. L.: A Method for Comparing Two Hierarchical Clusterings. Journal of the American Statistical Association, 78(383):553โ€“569, 1983.

TODO

  • add internal cluster indexes
  • complete References section
  • add definition and formula for each indexes

cluster_index's People

Contributors

n-serrette avatar

Stargazers

 avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.