Name
Title | ||
|---|---|---|
High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT. |
Abstract | ||
|---|---|---|
•A CP-HOPCM algorithm based on canonical polyadic decomposition is proposed.•The canonical polyadic decomposition in CP-HOPCM is used to compress the attributes.•A TT-HOPCM algorithm based on the tensor-network is proposed.•The tensor-network in TT-HOPCM is used to compress the attributes.•The proposed schemes compress the objects greatly without a high accuracy drop. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.inffus.2017.04.002 | Information Fusion |
Keywords | Field | DocType |
Big data,IoT,Possibilistic c-means clustering,Canonical polyadic decomposition,Tensor-train network | Data mining,Tensor,Computer science,Server,Theoretical computer science,Artificial intelligence,Cluster analysis,Data compression ratio,Internet of Things,Algorithm,Execution time,Big data,Machine learning,Limiting | Journal |
Volume | ISSN | Citations |
39 | 1566-2535 | 23 |
PageRank | References | Authors |
0.76 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingchen Zhang | 1 | 372 | 19.17 |
| Laurence T. Yang | 2 | 6870 | 682.61 |
| Zhikui Chen | 3 | 692 | 66.76 |
| Peng Li | 4 | 36 | 1.50 |