Name
Abstract | ||
|---|---|---|
We present a new L1-distance-based k-means clustering algorithm to address the challenge of clustering high-dimensional proportional vectors. The new algorithm explicitly incorporates proportionality constraints in the computation of the cluster centroids, resulting in reduced L1 error rates. We compare the new method to two competing methods, an approximate L1-distance k-means algorithm, where the centroid is estimated using cluster means, and a median L1 k-means algorithm, where the centroid is estimated using cluster medians, with proportionality constraints imposed by normalization in a second step. Application to clustering of projects based on distribution of labor hours by skill illustrates the advantages of the new algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4760982 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
k means,algorithm design and analysis,k means clustering,optimization,k means algorithm,error rate,resource management,estimation,approximation algorithms,clustering algorithms | Fuzzy clustering,CURE data clustering algorithm,Artificial intelligence,Cluster analysis,k-medians clustering,Canopy clustering algorithm,Mathematical optimization,Data stream clustering,Correlation clustering,Pattern recognition,Algorithm,Constrained clustering,Mathematics | Conference |
ISSN | Citations | PageRank |
1051-4651 | 9 | 0.96 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hisashi Kashima | 1 | 1739 | 118.04 |
| Jianying Hu | 2 | 478 | 35.52 |
| Bonnie K. Ray | 3 | 472 | 55.98 |
| Moninder Singh | 4 | 381 | 105.12 |