A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under the privacy preserving distributed data mining model, in which the data is distributed between two or more parties, and the parties cooperate to obtain the clustering results without revealing the data at the individual parties. In this paper, wedoi:10.1145/2320765.2320819 dblp:conf/edbt/LiuHLX12 fatcat:rci4iazpmjgr5eqtrsogejer5u