A fast and integrative algorithm for clustering performance evaluation in author name disambiguation

02/05/2021
by   Jinseok Kim, et al.
0

Author name disambiguation results are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting Lumping Error, and B-cubed. Although these measures have distinctive evaluation schemes, this paper shows that they can be calculated in a single framework by a set of common steps that compare truth and predicted clusters through two hash tables recording information about name instances with their predicted cluster indices and frequencies of those indices per truth cluster. This integrative calculation reduces greatly calculation runtime, which is scalable to a clustering task involving millions of name instances within a few seconds. During the integration process, B-cubed and K-metric are shown to produce the same precision and recall scores. In this framework, especially, name instance pairs for Pairwise-F are counted using a heuristic, surpassing a state-of-the-art algorithm in speedy calculation. Details of the integrative calculation are described with examples and pseudo-code to assist scholars to implement each measure easily and validate the correctness of implementation. The integrative calculation will help scholars compare similarities and differences of multiple measures before they select ones that characterize best the clustering performances of their disambiguation methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/15/2020

Evaluating and Validating Cluster Results

Clustering is the technique to partition data according to their charact...
research
08/10/2018

Effective Unsupervised Author Disambiguation with Relative Frequencies

This work addresses the problem of author name homonymy in the Web of Sc...
research
09/23/2021

Clustering performance analysis using new correlation based cluster validity indices

There are various cluster validity measures used for evaluating clusteri...
research
06/09/2017

Towards balanced clustering - part 1 (preliminaries)

The article contains a preliminary glance at balanced clustering problem...
research
09/27/2017

Scaling Author Name Disambiguation with CNF Blocking

An author name disambiguation (AND) algorithm identifies a unique author...
research
08/02/2023

A new approach for evaluating internal cluster validation indices

A vast number of different methods are available for unsupervised classi...
research
02/05/2021

Generating automatically labeled data for author name disambiguation: An iterative clustering method

To train algorithms for supervised author name disambiguation, many stud...

Please sign up or login with your details

Forgot password? Click here to reset