Analysing the segmentation of energy consumers using mixed fuzzy clustering

Schäfer, Hanna; Viegas, Joaquim L.; Ferreira, Marta C.; Vieira, Susana M.; Sousa, J.M.

doi:10.1109/fuzz-ieee.2015.7338120

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…CHI is an evaluation index based on data itself, is suitable for evaluating clustering results of dynamic data. Schkafer et al [5] analyzed the dynamic consumption data and static information data of users, clustered users by using hybrid fuzzy clustering algorithm, CHI, DBI and SC are used to evaluate the results of user clustering. In order to realize the division and management of different regions, Arroyo et al [6] analyzed the meteorological data of Spain, used K-Means and other clustering algorithms to cluster the regions, and used CHI, DBI and SC to evaluate the clustering results.…”

Section: Related Workmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.

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Section: Related Workmentioning

confidence: 99%