Efficiently mining frequent trees in a forest: algorithms and applications

By Mohammed J. Zaki. IEEE Transactions on Knowledge and Data Engineering, 2005

329 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fa2c570c29fd04066e3304dd1be0a07c4e314c60

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charles Sutton
2. Miltiadis Allamanis
3. Aliaksei Severyn
4. Michele A. A. Zito
5. Frans Coenen
6. Chuntao Jiang
7. Uri Alon
8. Eran Yahav

In Information Systems

1. Charles Sutton
2. Miltiadis Allamanis
3. Michele A. A. Zito
4. Frans Coenen
5. Chuntao Jiang
6. Aliaksei Severyn
7. Uri Alon
8. Eran Yahav

In Molecular Biology

1. Michele A. A. Zito
2. Frans Coenen
3. Chuntao Jiang
4. Aliaksei Severyn
5. Miltiadis Allamanis
6. Charles Sutton

Citations in the corpus

• A survey of frequent subgraph mining algorithms (2012, methodology): “An often cited example of a DLS-DS based algorithm is the SLEUTH Algorithm (Zaki 2005a).”
• Modelling input texts: from Tree Kernels to Deep Learning (2015, background): “Structural kernels have been successfully applied to ease the design of machine learning systems in diverse domains, ranging from bioinformatics [78,82,130] and data-mining [10, 30,139,162,173,181,184,191] to Natural Language Processing (NLP) [23,34,37,73,79,142].”
• Mining idioms from source code (2014, background): “This task is called frequent tree mining, and has been the subject of some work in the data mining literature [25, 49, 54, 55].”