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Title: Parallel object-oriented decision tree system

Abstract

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

Inventors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
908587
Patent Number(s):
7,007,035
Application Number:
09/877,570
Assignee:
The Regents of the University of California (Oakland, CA)
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Kamath, Chandrika, and Cantu-Paz, Erick. Parallel object-oriented decision tree system. United States: N. p., 2006. Web.
Kamath, Chandrika, & Cantu-Paz, Erick. Parallel object-oriented decision tree system. United States.
Kamath, Chandrika, and Cantu-Paz, Erick. 2006. "Parallel object-oriented decision tree system". United States. https://www.osti.gov/servlets/purl/908587.
@article{osti_908587,
title = {Parallel object-oriented decision tree system},
author = {Kamath, Chandrika and Cantu-Paz, Erick},
abstractNote = {A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.},
doi = {},
url = {https://www.osti.gov/biblio/908587}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 28 00:00:00 EST 2006},
month = {Tue Feb 28 00:00:00 EST 2006}
}

Works referenced in this record:

Design and implementation of a parallel object-oriented image processing toolkit
conference, October 2000

  • Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.
  • International Symposium on Optical Science and Technology, SPIE Proceedings
  • https://doi.org/10.1117/12.403590

MLC++: a machine learning library in C++
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  • Bruce, Andrew G.; Gao, Hong-Ye
  • SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, SPIE Proceedings
  • https://doi.org/10.1117/12.217582

A System for Induction of Oblique Decision Trees
journal, August 1994


Adaptive wavelet thresholding for image denoising and compression
journal, January 2000


Ideal spatial adaptation by wavelet shrinkage
journal, September 1994


ScalParC: a new scalable and efficient parallel classification algorithm for mining large datasets
conference, January 1998

  • Joshi, M. V.; Karypis, G.; Kumar, V.
  • Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing
  • https://doi.org/10.1109/IPPS.1998.669983