TY - GEN
T1 - K-landmarks
T2 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006
AU - Magdalinos, Panagis
AU - Doulkeridis, Christos
AU - Vazirgiannis, Michails
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Due to the vast amount and pace of high-dimensional data production and their distribution among network nodes, the fields of Distributed Knowledge Discovery (DKD) and Distributed Dimensionality Reduction (DDR) have emerged as a necessity in many application areas. While a wealth of centralized dimensionality reduction (DR) algorithms is available, only few have been proposed for distributed environments, most of them adaptations of centralized ones. In this paper, we introduce K-Landmarks, a new DDR algorithm, and we evaluate its comparative performance against a set of well known distributed and centralized DR algorithms. We primarily focus on each algorithm's performance in maintaining clustering quality throughout the projection, while retaining low stress values. Our algorithm outperforms most other algorithms, showing its suitability for highly distributed environments.
AB - Due to the vast amount and pace of high-dimensional data production and their distribution among network nodes, the fields of Distributed Knowledge Discovery (DKD) and Distributed Dimensionality Reduction (DDR) have emerged as a necessity in many application areas. While a wealth of centralized dimensionality reduction (DR) algorithms is available, only few have been proposed for distributed environments, most of them adaptations of centralized ones. In this paper, we introduce K-Landmarks, a new DDR algorithm, and we evaluate its comparative performance against a set of well known distributed and centralized DR algorithms. We primarily focus on each algorithm's performance in maintaining clustering quality throughout the projection, while retaining low stress values. Our algorithm outperforms most other algorithms, showing its suitability for highly distributed environments.
KW - Distributed dimension reduction
KW - Distributed knowledge discovery
U2 - 10.1007/11871637_32
DO - 10.1007/11871637_32
M3 - Conference contribution
AN - SCOPUS:33750363895
SN - 3540453741
SN - 9783540453741
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 322
EP - 334
BT - Knowledge Discovery in Databases
PB - Springer Verlag
Y2 - 18 September 2006 through 22 September 2006
ER -