Interpretable unsupervised decision trees

Details Interpretable unsupervised decision trees Interpretable unsupervised decision trees

2002-12-17

2009-06-02

Hirl, Joseph P. (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

C700S007000

Reexamination Certificate

active

07542960

ABSTRACT:
An unsupervised decision tree is constructed, involving the data records or patterns that do not posses any class labels. The objective of clustering or segmenting the data set is to discover subsets of data records that possess homogeneous characteristics. In the context of clustering, namely grouping or segmenting data sets without any supervised information, an interpretable decision tree is recognized as beneficial in various contexts such as customer profiling, text mining, and image and video categorization.

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