Name
Abstract | ||
|---|---|---|
Discovering connected regions in data space is a complex problem that is extremely demanding on the user. Datasets often require preprocessing and postprocessing before they are fit for algorithm and user consumption. Existing clustering algorithms require performance parameters to achieve adequate results. Typically, these parameters are either empirically optimized or scanned using brute force, which ultimately adds additional burden to the user. We present RADDACL2, a density-based clustering algorithm, with the intent of reducing overall user burden. The algorithm requires no information other than the dataset to identify clusters. In addition, the algorithm is deterministic, meaning the results will always be the same. Both of these features reduce user burden by decreasing the number of passes one must make to get an outcome. A number of experiments are performed using toy and real datasets to verify the capabilities of RADDACL2 as compared to existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s13748-015-0066-9 | Progress in AI |
Keywords | Field | DocType |
Clustering, Discovering connected regions in data space, Density, Non-linearly separable, Centroid, Segmentation, Deterministic, Neighborhood, Preclustering, Statistics | Data mining,Cluster (physics),Data space,Segmentation,Computer science,Brute force,Preprocessor,Artificial intelligence,Cluster analysis,Machine learning,Centroid,Recursion | Journal |
Volume | Issue | ISSN |
4 | 1-2 | 2192-6360 |
Citations | PageRank | References |
0 | 0.34 | 28 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Avila | 1 | 0 | 0.34 |
| Iren Valova | 2 | 136 | 25.44 |