Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data

2017 ◽  
pp. 477-486 ◽  
Author(s):  
Patrick G. Clark ◽  
Cheng Gao ◽  
Jerzy W. Grzymala-Busse ◽  
Teresa Mroczek
Keyword(s):  
Incomplete Data ◽  
Characteristic Sets

Complexity of Rule Sets in Mining Incomplete Data Using Characteristic Sets and Generalized Maximal Consistent Blocks

2018 ◽  
pp. 84-94
Author(s):  
Patrick G. Clark ◽  
Cheng Gao ◽  
Jerzy W. Grzymala-Busse ◽  
Teresa Mroczek ◽  
Rafal Niemiec
Keyword(s):  
Incomplete Data ◽  
Characteristic Sets ◽  
Rule Sets

Characteristic sets and generalized maximal consistent blocks in mining incomplete data

2018 ◽  
Vol 453 ◽  
pp. 66-79 ◽  
Author(s):  
Patrick G. Clark ◽  
Cheng Gao ◽  
Jerzy W. Grzymala-Busse ◽  
Teresa Mroczek
Keyword(s):  
Incomplete Data ◽  
Characteristic Sets

Mining Incomplete Data Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets and Maximal Consistent Blocks

2021 ◽  
pp. 3-17
Author(s):  
Patrick G. Clark ◽  
Jerzy W. Grzymala-Busse ◽  
Zdzislaw S. Hippe ◽  
Teresa Mroczek
Keyword(s):  
Incomplete Data ◽  
Characteristic Sets

Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks

2020 ◽  
Author(s):  
Patrick G Clark ◽  
Cheng Gao ◽  
Jerzy W Grzymala-Busse ◽  
Teresa Mroczek ◽  
Rafal Niemiec
Keyword(s):  
Data Mining ◽  
Error Rate ◽  
Incomplete Data ◽  
Cross Validation ◽  
Rule Induction ◽  
Data Sets ◽  
Main Criterion ◽  
Characteristic Sets ◽  
Rule Sets ◽  
Fold Cross Validation

Abstract In this paper, missing attribute values in incomplete data sets have three possible interpretations: lost values, attribute-concept values and ‘do not care’ conditions. For rule induction, we use characteristic sets and generalized maximal consistent blocks. Therefore, we apply six different approaches for data mining. As follows from our previous experiments, where we used an error rate evaluated by ten-fold cross validation as the main criterion of quality, no approach is universally the best. Thus, we decided to compare our six approaches using complexity of rule sets induced from incomplete data sets. We show that the smallest rule sets are induced from incomplete data sets with attribute-concept values, while the most complicated rule sets are induced from data sets with lost values. The choice between interpretations of missing attribute values is more important than the choice between characteristic sets and generalized maximal consistent blocks.

Corrigendum to “Bayesian test for asymmetry and nonstationarity in MTAR model with possibly incomplete data”

2006 ◽  
Vol 50 (2) ◽  
pp. 584
Author(s):  
Soo Jung Park ◽  
Dong Wan Shin ◽  
Byeong Uk Park ◽  
Woo Chul Kim ◽  
Man-Suk Oh
Keyword(s):  
Incomplete Data ◽  
Bayesian Test
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