Distributed Knowledge Discovery with Non Linear Dimensionality Reduction Authors: Panagis Magdalinos, Michalis Vazirgiannis, Dialecti Valsamou Keywords: distributed non linear dimensionality reduction, NLDR, distributed dimensionality reduction, DDR, distributed data mining, DDM, dimensionality reduction, DR, Distributed Isomap, D-Isomap, C-Isomap, L-Isomap
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.
Data mining in peer-to-peer networks will face topic map authors sooner or later.
Not only a useful discussion of the issues, but, the authors have posted source code and data sets used in the article as well: