{"id":1658,"date":"2019-06-20T15:12:56","date_gmt":"2019-06-20T15:12:56","guid":{"rendered":"http:\/\/uni.hi.is\/helmut\/?p=1658"},"modified":"2020-01-24T15:10:24","modified_gmt":"2020-01-24T15:10:24","slug":"datasets-for-dbscan-evaluation","status":"publish","type":"post","link":"https:\/\/uni.hi.is\/helmut\/2019\/06\/20\/datasets-for-dbscan-evaluation\/","title":{"rendered":"Datasets for DBSCAN evaluation"},"content":{"rendered":"

For evaluating implementations of the popular DBSCAN<\/a> clustering algorithm, various publications use several datasets. Pointers to these datasets and information on paramaters (e.g. normalisation, epsilon and minpts) are collected here. You are welcome to contact me if you have further (big) datasets that are good benchmarks for DBSCAN.<\/p>\n

Sarma et al.: \u03bcDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality<\/a><\/h3>\n

TODO: check in detail datasets used, but some are those datasets used in some of the other publications below, but \"In addition, we have also used a few other real datasets: 3D Road Network (3DSRN) [32] contains vechicular GPS data; Household Power (HHP*) and KDDBIO145K (KDDB*) datasets are borrowed from UCI Repository [33].\"<\/p>\n

Gan, Tao: DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation<\/a><\/h3>\n

Data normalized to [0, 10^5 ] for every dimension.<\/p>\n

MinPts = 100, Epsilon = 5000 and higher. (Note: far too high value turning almost the entire dataset into a single cluster -- the mis-claim is on their side!).<\/p>\n

Their preprocessed datasets<\/p>\n