Name
Abstract | ||
|---|---|---|
The effectiveness of change-detection algorithms is often assessed on real-world datasets by injecting synthetically generated changes. Typically, the magnitude of the introduced changes is not controlled, and most of experimental practices lead to results that are difficult to reproduce and compare with. This problem becomes particularly relevant when the data-dimension scales, as it happens in big data applications. |
Year | Venue | Field |
|---|---|---|
2016 | INNS Conference on Big Data | Magnitude (mathematics),Clustering high-dimensional data,Bisection method,Change detection,Computer science,Algorithm,Big data |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cesare Alippi | 1 | 1040 | 115.84 |
| Giacomo Boracchi | 2 | 324 | 30.49 |
| Diego Carrera | 3 | 43 | 7.09 |