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Table 4 The diversity and quality analysis of the three ensemble-based methods

From: Cluster ensemble based on Random Forests for genetic data

Dataset

Method

DS(P)

Q(P)

Q(P*)

Q(P*)-Q(P)

HapMap

AWcluE

0.1912

0.8139

0.9148

0.1009

PCAcluE

0.1429

0.7078

0.7510

0.0432

RFcluE

0.2697

0.7823

0.9353

0.1529

Pan-Asian

AWcluE

0.1802

0.8356

0.9497

0.1141

PCAcluE

0.0801

0.8427

0.8933

0.0506

RFcluE

0.2800

0.7794

0.9620

0.1826

Shriver’s

AWcluE

0.1164

0.8804

0.8879

0.0074

PCAcluE

0.0896

0.8236

0.8351

0.0115

RFcluE

0.1543

0.8592

0.9204

0.0612

  1. The table shows the diversity and quality of the base clusterings (denoted by DS (P) and Q (P), respectively) along with the quality of the ensemble’s final clustering, Q (P ∗), for three datasets using the three ensemble-based clustering methods: PCAcluE, AWcluE, and RFcluE