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:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 908587
- Patent Number(s):
- 7007035
- Application Number:
- 09/877,570
- Assignee:
- The Regents of the University of California (Oakland, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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. Tue .
"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 = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2006},
month = {2}
}
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