BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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In this work the ''Density Based Spatial Clustering of Applications with Noise'' (DBSCAN) algorithm was adapted to early stage DNA damage clustering calculations. The resulting algorithm takes into account the distribution of energy deposit induced by ionising particles and a damage probability function that depends on the total energy deposit amount. Proton track simulations were carried out in small micrometric volumes representing small DNA containments. The algorithm was used to determine the damage concentration clusters and thus to deduce the DSB/SSB ratios created by protons between 500keV and 50MeV. The obtained results are compared to other calculations and to available experimental data of fibroblast and plasmid cells irradiations, both extracted from literature.