Name
Abstract | ||
|---|---|---|
Current data processing tasks require efficient approaches capable of dealing with large databases. A promising strategy consists in distributing the data along with several computers that partially solve the undertaken problem. Finally, these partial answers are integrated to obtain a final solution. We introduce distributed shared nearest neighbors (<italic>D-SNN</italic>), a novel clustering algorithm that work with disjoint partitions of data. Our algorithm produces a global clustering solution that achieves a competitive performance regarding centralized approaches. The algorithm works effectively with high dimensional data, being advisable for document clustering tasks. Experimental results over five data sets show that our proposal is competitive in terms of quality performance measures when compared to state of the art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2949455 | IEEE ACCESS |
Keywords | DocType | Volume |
Clustering algorithms, Distributed databases, Partitioning algorithms, Task analysis, Computers, Parallel processing, Approximation algorithms, Distributed algorithms, distributed text clustering, high dimensional data | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Zamora | 1 | 0 | 0.34 |
| Héctor Allende-cid | 2 | 22 | 12.60 |
| Marcelo Mendoza | 3 | 1502 | 85.81 |