Name
Abstract | ||
|---|---|---|
In spectral datasets, such as those consisting of MR spectral data derived from MS lesions, neighboring features tend to be
highly correlated, suggesting the data lie on some low-dimensional space. Naturally, finding such low-dimensional space is
of interest. Based on this real-life problem, this paper extracts an abstract problem, neighboring feature clustering (NFC).
Noticeably different from traditional clustering schemes where the order of features doesn’t matter, NFC requires that a cluster
consist of neighboring features, that is features that are adjacent in the original feature ordering. NFC is then reduced
to a piece-wise linear approximation problem. We use minimum description length (MDL) method to solve this reduced problem.
The algorithm we proposed works well on synthetic datasets. NFC is an abstract problem. With minor changes, it can be applied
to other fields where the problem of finding piece-wise neighboring groupings in a set of unlabeled data arises.
|
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11752912_79 | Hellenic Conference on Artificial Intelligence |
Keywords | Field | DocType |
mr spectral data,piece-wise linear approximation problem,neighboring feature,neighboring feature clustering,reduced problem,real-life problem,low-dimensional space,abstract problem,unlabeled data,piece-wise neighboring grouping,minimum description length | Linear approximation,Data mining,Computer science,Minimum description length,Algorithm,Spectral data,Artificial intelligence,FLAME clustering,Cluster analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
3955 | 0302-9743 | 3-540-34117-X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhifeng Wang | 1 | 0 | 0.34 |
| Wei Zheng | 2 | 94 | 8.34 |
| Yuhang Wang | 3 | 159 | 16.49 |
| James Ford | 4 | 227 | 16.26 |
| Fillia Makedon | 5 | 1676 | 201.73 |
| Justin D. Pearlman | 6 | 2 | 2.43 |