• Corpus ID: 244527396

Two step clustering for data reduction combining DBSCAN and k-means clustering

  title={Two step clustering for data reduction combining DBSCAN and k-means clustering},
  author={Bart J. J. Kremers and Aaron J. Ho and Jonathan Citrin and K. van de Plassche},
A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection of high data density regions and the kmeans algorithm for reduction. The proposed algorithm iterates while successively decrementing the DBSCAN search radius, allowing for an adaptive reduction factor based on the effective data density. The… 

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