Density peaks clustering based on balance density and connectivity

Highlights：

• A novel density peaks clustering algorithm called BC-DPC is proposed based on mutual nearest neighbor and connectivity, which can quickly find the correct cluster centers and obtain satisfactory clustering results.

• A new definition of density is proposed, which can elimilate the density difference among different clusters and accurately estimate the density of the data points.

• This algorithm fully considers the density change and connectivity between data points when calculating the relative distance. This makes the domino effect can be avoided in the clustering process.

• The algorithm proposes a novel fast search strategy, which is used to calculate relative distance of data points. This strategy makes the efficiency of the proposed algorithm greatly improved.

• The performance of the proposed algorithm is compared to DPC and five improved DPC in synthetic, UCI, and image datasets.

• The experimental results show the effectiveness and the efficiency of the proposed algorithm.