DBSCAN Clustering Algorithm Based on Locality Sensitive Hashing
Ye Shiqiu, Zhu Qingsheng · Journal of Physics Conference Series · 2019
The traditional DBSCAN clustering algorithm runs less efficiently on large data sets and high dimensional data sets. Aiming at the disadvantages of this, a core point selection algorithm and isolated points detection algorithm based on locality sensitive hashing is proposed. After that a traditional DBSCAN algorithm is run on the core points. Finally, the remaining points are assigned to the same category of the core points which both are in the same sub-cluster. The experiment results show that the proposed algorithm maintains a high correct rate on synthetic and real data sets and the efficiency is greatly improved.