Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Visualizing global manifold based on distributed local data abstractions

Language

English

Abstract

Mining distributed data for global knowledge is getting more attention recently. The problem is especially challenging when data sharing is prohibited due to local constraints like limited bandwidth and data privacy. In this paper, we investigate how to derive the embedded manifold (as a 2-D map) for a horizontally partitioned data set, where data cannot be shared among the partitions directly. We propose a model-based approach which computes hierarchical local data abstractions, aggregates the abstractions, and finally learns a global generative model - generative topographic mapping (GTM) based on the aggregated data abstraction. We applied the proposed method to two benchmarking data sets and demonstrated that the accuracy of the derived manifold can effectively be controlled by adjusting the data granularity level of the adopted local abstraction.

Keywords

Data visualization, Data privacy, Data mining, Bandwidth, Computer science, Aggregates, Automatic control, Memory, Parametric statistics, Covariance matrix

Publication Date

11-2005

Source Publication Title

Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM’05)

Editors

Han, Jiawei ; Wah, Benjamin W. ; Raghavan, Vijay ; Wu, Xindong ; Rastogi, Rajeev

Conference Location

Houston, United States

Publisher

IEEE

Peer Reviewed

1

Copyright

Copyright © 2005 by The Institute of Electrical and Electronics Engineers, Inc.

Funder

This work is jointly supported by RGC Central Allocation Research Grant (HKBU 2/03/C) and Hong Kong Baptist University FRG Grant (FRG/05-06/I-16).

DOI

10.1109/ICDM.2005.150

Link to Publisher's Edition

http://dx.doi.org/10.1109/ICDM.2005.150

ISSN (print)

15504786

ISSN (electronic)

23748486

ISBN (print)

9780769522784

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