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505
gSpan: Graph-Based Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
Abstract
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Cited by 650 (34 self)
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We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs
Graph-Based Substructure Pattern Mining Using CUDA Dynamic Parallelism
"... Abstract. CUDA is an advanced massively parallel computing platform that can provide high performance computing power at much more affordable cost. In this paper, we present a parallel graph-based substructure pattern mining al-gorithm using CUDA Dynamic Parallelism. The key contribution is a parall ..."
Abstract
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Cited by 1 (0 self)
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Abstract. CUDA is an advanced massively parallel computing platform that can provide high performance computing power at much more affordable cost. In this paper, we present a parallel graph-based substructure pattern mining al-gorithm using CUDA Dynamic Parallelism. The key contribution is a
An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data
, 2000
"... This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the exte ..."
Abstract
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Cited by 310 (7 self)
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This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through
Bangalore.
"... Frequent sub graph mining is another active research topic in data mining. A graph is a general model to represent data and has been used in many domains like chemo informatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as sub graph t ..."
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testing, generally have higher time complexity than the corresponding operations on item sets, sequences, and trees. We investigated new approaches for frequent graph-based pattern mining in graph datasets and found that a novel algorithm called span (graph-based Substructure pattern mining), has been
Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction
- IN PROC. OF THE 5TH INTERNATIONAL CONFERENCE ON DISCOVERY SCIENCEDISCOVEERY (DS 2002
, 2002
"... Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search strategy but at the same time it su#ers from the incompleteness of search. Improvement is made on its search capability without i ..."
Abstract
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Cited by 8 (4 self)
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Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search strategy but at the same time it su#ers from the incompleteness of search. Improvement is made on its search capability without
Application of graph-based data mining to metabolic pathways. Workshop on Data Mining
- in Bioinfomratics, IEEE International Conference on Data Mining, December 18-22, 2006, Hong Kong, 2006. JM701130Z 654 Journal of Medicinal Chemistry
"... We present a method for finding biologically meaning-ful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data that has been generated by long-term re-search encourages us to move our focus to a systems-level understanding of bio-sys ..."
Abstract
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Cited by 7 (5 self)
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We present a method for finding biologically meaning-ful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data that has been generated by long-term re-search encourages us to move our focus to a systems-level understanding of bio
Graph-based mining of multiple object usage patterns
- In Proceedings of ESEC/FSE ’09
, 2009
"... The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That cr ..."
Abstract
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Cited by 43 (4 self)
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. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects. In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object
Graph Indexing: A Frequent Structure-based Approach
, 2004
"... Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. In this paper, we investigate the is ..."
Abstract
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Cited by 201 (25 self)
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the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Di#erent from the existing path-based methods, our approach, called gIndex, makes use of frequent substructure as the basic indexing feature. Frequent substructures are ideal candidates since they explore
Using a Graph-Based Approach for Discovering Cybercrime
"... The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. While there has been some work ..."
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The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. While there has been some
Graph-based hierarchical conceptual clustering
- International Journal on Artificial Intelligence Tools
, 2001
"... Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we ..."
Abstract
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Cited by 32 (5 self)
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Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we
Results 1 - 10
of
505
