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Classification through Maximizing Density
San Jose, California November 29-December 02
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989596First IEEE International Conference o ...
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Hui Wan, David Bell, Diu Liu, Ivo Düntsch, "Classification through Maximizing Density," Data Mining, IEEE International Conference on, pp. 655, First IEEE International Conference on Data Mining (ICDM'01), 2001.
 
 
BibTex
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@article{ 10.1109/ICDM.2001.989596,
author = {Hui Wan and David Bell and Diu Liu and Ivo Düntsch},
title = {Classification through Maximizing Density},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2001},
isbn = {0-7695-1119-8},
pages = {655},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989596},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Classification through Maximizing Density
SN - 0-7695-1119-8
SP
EP
A1 - Hui Wan,
A1 - David Bell,
A1 - Diu Liu,
A1 - Ivo Düntsch,
PY - 2001
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
This paper presents a novel method for classification, which makes use of the models built by the lattice machine (LM) [1,3 ]. The LM approximates data resulting in, as a model of data, a set of hyper tuples that are equilabelled, supported and maximal . The method presented in this paper uses the LM model of data to classify new data with a view to maximising the density of the model. Experiments show that this method, when used with the LM, outperforms the C2 algorithm in [3 ] and it is comparable to the C5.0 classification algorithm.
Citation:
Hui Wan, David Bell, Diu Liu, Ivo Düntsch, "Classification through Maximizing Density," icdm, pp.655, First IEEE International Conference on Data Mining (ICDM'01), 2001
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