Algorithms for String Pattern Discovery

2007 ◽  
pp. 24-29
Author(s):  
Hideo Bannai
Keyword(s):  
Pattern Discovery ◽  
String Pattern

Character string pattern matching in Algol 68

1977 ◽  
Vol 12 (6) ◽  
pp. 144-152 ◽  
Author(s):  
R. J.W. Housden ◽  
N. Kotarski
Keyword(s):  
Pattern Matching ◽  
Character String ◽  
String Pattern ◽  
Algol 68

Character string pattern matching in Algol 68

1977 ◽  
Author(s):  
R. J.W. Housden ◽  
N. Kotarski
Keyword(s):  
Pattern Matching ◽  
Character String ◽  
String Pattern ◽  
Algol 68

scholarly journals Pattern discovery and disentanglement on relational datasets

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew K. C. Wong ◽  
Pei-Yuan Zhou ◽  
Zahid A. Butt
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Case Studies ◽  
Prediction Accuracy ◽  
Pattern Discovery ◽  
Explicit Representation ◽  
Human Cognition ◽  
Source Level ◽  
Low Volume

AbstractMachine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

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