Graph-Based Data Mining

By Diane Joyce Cook, Lawrence B. Holder. IEEE Intelligent Systems, 2000

499 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9dd0da9458021ed43056d5009eb5932cf86ea90e

How researchers see this paper

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

In Computer Vision and Pattern Recognition

In Information Systems

In Signal Processing

Citations in the corpus

Link mining: a survey (2005, methodology): “Traditional data mining algorithms such as association rule mining, market basket analysis, and cluster analysis commonly attempt to .nd patterns in a dataset characterized by a collection of independent instances of a single relation.”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, methodology): “Even though there are a number of parameters controlling SUBDUE’s heuristic search algorithm, the most critical among them are the width of the beam search, the maximum size of the discovered subgraph, and the total number of subgraphs to be discovered.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “To narrow the search-space and improve its computational efficiency, SUBDUE uses a heuristic beam search approach, which quite often results in failing to find subgraphs that are frequent.”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “The most well-known algorithm for finding recurring subgraphs in a single large graph is the SUBDUE system, originally developed in 1994, and improved over the years (Holder et al., 1994; Cook and Holder, 1994, 2000; Cook et al., 1995, 2000).”
Mining the network value of customers (2001, background): “[9] D. J. Cook and L. B. Holder.”
Link-Based Classification (2003, background): “Link mining is a newly emerging research area that is at the intersection of the work in link analysis [10, 16], hypertext and web mining [3], relational learning and inductive logic programming [9] and graph mining [ 5 ].”
A survey of frequent subgraph mining algorithms (2012, methodology): “However, one frequently quoted example is the SUBDUE algorithm (Cook & Holder 1994,2000).”
ANF: a fast and scalable tool for data mining in massive graphs (2002, methodology): “Systems for this include SUBDUE [5] and AGM [11].”
An efficient algorithm for discovering frequent subgraphs (2004, methodology): “Probably the most well-known heuristic-based approach is the SUBDUE system, originally developed in 1994, but has been improved over the years [23], [8].”
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs (2016, methodology): “The method used the SUBDUE [11] system to first discover frequent substructures in the database in an offline fashion.”
Outlier Analysis (2013, methodology): “A second approach is based more directly on the SUBDUE method [123].”
Graph-based anomaly detection (2003, methodology): “As with many algorithms involving graphs, the time complexity of Subdue is exponential in the worst case, but can be reduced to polynomial in practice [1].”