| Alternate document: Details Collective Data Mining From Distributed, Vertically Partitioned Feature Space (98) Hillol Kargupta, Erik Johnson, Eleonora Riva |
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Abstract: This paper introduces the collective data mining
(CDM), a unique approach to distributed data mining
(DDM) from heterogeneous sites. It points out
that naive approaches to distributed data analysis in
a heterogeneous environment may face ambiguous situation
and may lead to incorrect global data model.
It also observes that any function can be expressed in
a distributed fashion using a set of appropriate basis
functions and orthonormal basis functions can be effectively
used for developing a... (Update)
Context of citations to this paper: More
.... on the internet, the JAM project [12] for developing a java agent based meta learning framework for distributed mining and the BODHI project [7] for doing collective data mining with stress on learning from vertically partitioned data. The field of distributed data mining shares...
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BibTeX entry: (Update)
H. Kargupta and B. Park. The collective data mining: A technology for ubiquitous data analysis from distributed heterogeneous sites. Submitted to IEEE Computer Special Issue on Data Mining, 1998. http: //www.eecs.wsu.edu/~hillol/ddm.html. http://citeseer.ist.psu.edu/kargupta98collective.html More
@misc{ kargupta98collective,
author = "H. Kargupta and B. Park",
title = "The collective data mining: A technology for ubiquitous data analysis from
distributed heterogeneous sites",
text = "H. Kargupta and B. Park. The collective data mining: A technology for ubiquitous
data analysis from distributed heterogeneous sites. Submitted to IEEE Computer
Special Issue on Data Mining, 1998. http: //www.eecs.wsu.edu/~hillol/ddm.html.",
year = "1998",
url = "citeseer.ist.psu.edu/kargupta98collective.html" }
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