Name
Abstract | ||
|---|---|---|
The clustering method used in this paper is developed for interval datasets encountered in most applications. Hausdorff distance metric defined for interval data is used in measuring distance. The method aims to cluster real time streaming data. Thus, sequential clustering method is used and to overcome the disadvantages associated with it, queue structure is used. To improve the clustering performance, cluster merging and deletion is also realized after clustering. |
Year | Venue | Keywords |
|---|---|---|
2018 | Signal Processing and Communications Applications Conference | interval data,sequential clustering,hyper-rectangle,prototype,Hausdorff distance |
Field | DocType | ISSN |
Radar,Pattern recognition,Computer science,Queue,Algorithm,AC power,Hausdorff distance,Artificial intelligence,Streaming data,Cluster analysis,Merge (version control),Interval data | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuat Cogun | 1 | 0 | 0.34 |
| Fatih Altiparmak | 2 | 39 | 5.56 |
| Halim Sinan Balaban | 3 | 0 | 0.34 |