Results 1  10
of
100
Substructure Discovery Using Minimum Description Length and Background Knowledge
 Journal of Artificial Intelligence Research
, 1994
"... The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures ..."
Abstract

Cited by 185 (43 self)
 Add to MetaCart
(Show Context)
The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previouslydiscovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationallybounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by Subdue to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate Subdu...
Algorithmics and Applications of Tree and Graph Searching
 In Symposium on Principles of Database Systems
, 2002
"... Modern search engines answer keywordbased queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domainindependent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree an ..."
Abstract

Cited by 141 (8 self)
 Add to MetaCart
(Show Context)
Modern search engines answer keywordbased queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domainindependent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in nextgeneration database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work.
Fingerprint classification by directional image partitioning
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1999
"... In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into “homogeneous ” connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set ..."
Abstract

Cited by 77 (0 self)
 Add to MetaCart
(Show Context)
In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into “homogeneous ” connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness.
Unsupervised Category Modeling, Recognition, and Segmentation in Images
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2008
"... paper is aimed at simultaneously solving the following related problems: 1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category, 2) learning a regionbased structural model of the ..."
Abstract

Cited by 34 (9 self)
 Add to MetaCart
(Show Context)
paper is aimed at simultaneously solving the following related problems: 1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category, 2) learning a regionbased structural model of the
Graphbased hierarchical conceptual clustering
 International Journal on Artificial Intelligence Tools
, 2001
"... Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graphbased 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

Cited by 32 (5 self)
 Add to MetaCart
Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graphbased 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 present Subdue and focus on its clustering capabilities. We use two examples to illustrate the validity of the approach both in structured and unstructured domains, as well as compare Subdue to an earlier clustering algorithm.
WeisfeilerLehman Graph Kernels
, 2010
"... In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the WeisfeilerLehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture ..."
Abstract

Cited by 27 (3 self)
 Add to MetaCart
(Show Context)
In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the WeisfeilerLehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this WeisfeilerLehman sequence of graphs, including a highly efficient kernel comparing subtreelike patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the WeisfeilerLehman graph sequence. In our experimental evaluation, our kernels outperform stateoftheart graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Our kernels open the door to largescale applications of graph kernels in various disciplines such as computational biology and social network analysis. Keywords: graph kernels, graph classification, similarity measures for graphs, WeisfeilerLehman algorithm
A Survey of Frequent Subgraph Mining Algorithms
 THE KNOWLEDGE ENGINEERING REVIEW
, 2004
"... Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplica ..."
Abstract

Cited by 27 (1 self)
 Add to MetaCart
Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.
Scalable Discovery Of Informative Structural Concepts Using Domain Knowledge
 IEEE Expert
, 1996
"... Discovering repetitive, and functional substructures in large structural databases improves the ability to interpret and compress the data. However, scientists working with a database in their area of expertise often search for predetermined types of structures, or for structures exhibiting characte ..."
Abstract

Cited by 26 (18 self)
 Add to MetaCart
(Show Context)
Discovering repetitive, and functional substructures in large structural databases improves the ability to interpret and compress the data. However, scientists working with a database in their area of expertise often search for predetermined types of structures, or for structures exhibiting characteristics specific to the domain. This paper presents a method for guiding the discovery process with domainspecific knowledge. In this paper, the Subdue discovery system is used to evaluate the benefits of using domain knowledge to guide the discovery process. Results show that domainspecific knowledge improves the search for substructures which are useful to the domain, and leads to greater compression of the data. Empirical and theoretical results also indicate the scalability of the algorithm to increasingly large structural databases. Keywordsdata mining, minimum description length principle, data compression, inexact graph match, domain knowledge, scalability Supported by NASA gran...
StructureBased Similarity Search with Graph Histograms
 In Proceedings of the 10th International Workshop on Database & Expert Systems Applications
, 1999
"... Objects like road networks, CAD/CAM components, electrical or electronic circuits, molecules, can be represented as graphs, in many modern applications. In this paper, we propose an efficient and effective graph manipulation technique that can be used in graphbased similarity search. Given a query ..."
Abstract

Cited by 24 (0 self)
 Add to MetaCart
Objects like road networks, CAD/CAM components, electrical or electronic circuits, molecules, can be represented as graphs, in many modern applications. In this paper, we propose an efficient and effective graph manipulation technique that can be used in graphbased similarity search. Given a query graph G q (V; E), we would like to determine fast which are the graphs in the database that are similar to G q (V; E), with respect to a similarity measure. First, we study the similarity measure between two graphs. Then, we discuss graph representation techniques by means of multidimensional vectors. It is shown that no false dismissals are introduced by using the vector representation. Finally we illustrate some representative queries that can be handled by our approach, and present experimental results, based on the proposed graph similarity algorithm. The results show that considerable savings are obtained with respect to computational effort and I/O operations, in comparison to conventional searching techniques.