Validity index for crisp and fuzzy clusters

By M. K. Pakhira, Sanghamitra Bandyopadhyay, Ujjwal Maulik. Pattern Recognition, 2004

772 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/24d65184b9e770af59eb17fec48ecd53bbaaa556

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ricardo José Gabrielli Barreto Campello
2. Jing Lin
3. Eduardo R. Hruschka
4. Yaguo Lei
5. Weina Wang
6. Naipeng Li
7. Lucas Vendramin
8. Yunjie Zhang

In Statistical and Nonlinear Physics

1. Ricardo José Gabrielli Barreto Campello

In Signal Processing

1. Jing Lin
2. Eduardo R. Hruschka
3. Ricardo José Gabrielli Barreto Campello
4. Yaguo Lei

Citations in the corpus

• A Model-Based Method for Remaining Useful Life Prediction of Machinery (2016, methodology): “In this algorithm, a PBM index, which is commonly used for verifying the cluster validity [35], is employed to determine an appropriate number of clusters.”
• On fuzzy cluster validity indices (2007, background): “proposed a validity index called the PBM index [37].”
• Relative clustering validity criteria: A comparative overview (2010, background): “So, the best partition should be indicated when PBM is maximized, which implies maximizing D K while minimizing E K .”
• A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, neutral): “index [295,296], root-mean-square standard deviation index [295,296], RS index [297], PBM index [300] and SV index [301].”