Name
Abstract | ||
|---|---|---|
Data mining tasks results are usually improved by reducing the dimensionality of data This improvement however is achieved harder in the case that data lay on a non linear manifold and are distributed across network nodes Although numerous algorithms for distributed dimensionality reduction have been proposed, all assume that data reside in a linear space In order to address the non-linear case, we introduce D-Isomap, a novel distributed non linear dimensionality reduction algorithm, particularly applicable in large scale, structured peer-to-peer networks Apart from unfolding a non linear manifold, our algorithm is capable of approximate reconstruction of the global dataset at peer level a very attractive feature for distributed data mining problems We extensively evaluate its performance through experiments on both artificial and real world datasets The obtained results show the suitability and viability of our approach for knowledge discovery in distributed environments. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13672-6_2 | PAKDD (2) |
Keywords | Field | DocType |
data mining tasks result,knowledge discovery,attractive feature,approximate reconstruction,non-linear case,non linear dimensionality reduction,data mining problem,non linear manifold,numerous algorithm,dimensionality reduction,linear space,data mining,distributed environment | Distributed knowledge,Data mining,Dimensionality reduction,Computer science,Linear space,Node (networking),Curse of dimensionality,Distributed algorithm,Knowledge extraction,Artificial intelligence,Manifold,Machine learning | Conference |
Volume | ISSN | ISBN |
6119 | 0302-9743 | 3-642-13671-0 |
Citations | PageRank | References |
3 | 0.41 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panagis Magdalinos | 1 | 35 | 5.55 |
| Michalis Vazirgiannis | 2 | 3942 | 268.00 |
| Dialecti Valsamou | 3 | 3 | 0.41 |