GF-DBSCAN : A New Efficient and Effective Data Clustering Technique for Large Databases

@inproceedings{Tsai2009GFDBSCANA,
  title={GF-DBSCAN : A New Efficient and Effective Data Clustering Technique for Large Databases},
  author={Cheng-Fa Tsai and WU CHIEN-TSUNG},
  year={2009}
}
The DBSCAN data clustering accurately searches adjacent area with similar density of data, and effectively filters noise, making it very valuable in data mining. However, DBSCAN needs to compare all data in each object, making it very time-consuming. This work presents a new clustering method called GF-DBSCAN, which is based on a well-known existing approach named FDBSCAN. The new algorithm is grid-based to reduce the number of searches, and redefines the cluster cohesion merging, giving it… CONTINUE READING
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