A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

. 2019 Dec 31;20(1):238. doi: 10.3390/s20010238

Algorithm 2 Clustering with at least one overlapping cluster in dataset

Z

Input: dataset Z. Output: cluster

C_{i}

and its cluster center

z_{i}

i \in {1, 2, \dots, k}

2.1: Calculate

δ_{i}

according to (11), and we can obtain estimated cluster centers

{\overset{⌢}{z}}_{i}

i \in {1, 2, \dots, k}

using the DPC algorithm.

2.2: According to (16), for cluster centers

{\overset{⌢}{z}}_{i}

that are

\max (ρ_{i}) < 1.1 \times r

, the cluster center is

{\overset{⌢}{z}}_{i}

; for cluster centers

{\overset{⌢}{z}}_{i}

that are

\max (ρ_{i}) \geq 1.1 \times r

, calculate

C_{i}

and

k_{i}

according to (12) and (13), then cluster

C_{i}

with the k-means algorithm (

k_{i}

clusters).

2.3: Repeat step 2.2, until all the overlapping clusters are all divided into sub-clusters.

2.4: Revisit each cluster

C_{i}

to make sure that the CL constraint was satisfied, then calculate the cluster center

z_{i}

of each cluster.