Name
Abstract | ||
|---|---|---|
In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients and preserve the clusters well. The grid based clustering algorithm works well with DCT transformed data, demonstrating the viability of DCT for data stream clustering applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3745/JIPS.2010.6.1.067 | JOURNAL OF INFORMATION PROCESSING SYSTEMS |
Keywords | Field | DocType |
Grid Density-Based Clustering, Approximate Cluster Analysis, Discrete Cosine Transform, Sampling, Data Reconstruction, Data Compression | Data stream mining,Data stream clustering,Pattern recognition,Computer science,Modified discrete cosine transform,Discrete cosine transform,Artificial intelligence,Sampling (statistics),Data compression,Cluster analysis,Grid | Journal |
Volume | Issue | ISSN |
6 | 1 | 1976-913X |
Citations | PageRank | References |
4 | 0.41 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Yu | 1 | 20 | 4.40 |
| Damalie Oyana | 2 | 4 | 0.75 |
| Wen-Chi Hou | 3 | 387 | 274.15 |
| Michael Wainer | 4 | 17 | 8.26 |