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Symbolic Graph Embedding Using Frequent Pattern Mining [chapter]

Blaž Škrlj, Nada Lavrač, Jan Kralj
2019 Lecture Notes in Computer Science  
Such patterns are in this work used as features to represent individual nodes, yielding interpretable, symbolic node embeddings.  ...  We propose Symbolic Graph Embedding (SGE), an algorithm aimed to learn symbolic node representations.  ...  Acknowledgements We acknowledge the financial support from the Slovenian Research Agency through core research programmes P2-0103 and P6-0411 and project Semantic Data Mining for Linked Open Data Slovenian  ... 
doi:10.1007/978-3-030-33778-0_21 fatcat:o5ayyrihyfhptcwouycdjw7one

An efficient graph-mining method for complicated and noisy data with real-world applications

Yi Jia, Jintao Zhang, Jun Huan
2011 Knowledge and Information Systems  
In this paper, we present a novel graph database-mining method called APGM (APproximate Graph Mining) to mine useful patterns from noisy graph database.  ...  Keywords Graph mining · Approximate subgraph isomorphism Introduction Frequent subgraph mining is an active research topic in the data-mining community.  ...  The goal of graph database mining is to locate useful and interpretable patterns in a large volume of graph data.  ... 
doi:10.1007/s10115-010-0376-y fatcat:fkmqyt4j5jgorbwm6f72fg7ncy

A Comparative Study Of Gtc And Psp Algorithms For Mining Sequential Patterns Embedded In Database With Time Constraints

Safa Adi
2018 Zenodo  
This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms).  ...  Experiments show that the hybrid approach is very efficient for short, frequent sequences.  ...  GTC use to build a sequence graph biased on time constraints.  ... 
doi:10.5281/zenodo.1340597 fatcat:kxpqz5futjhlvnhicmfkjngzze

A survey of frequent subgraph mining algorithms

Chuntao Jiang, Frans Coenen, Michele Zito
2012 Knowledge engineering review (Print)  
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.  ...  This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.  ...  Chopper first uses a revised PrefixSpan (Pei et al. 2001) to mine frequent sequential patterns.  ... 
doi:10.1017/s0269888912000331 fatcat:pxye65ayvzgevplfpkjhwissn4

Clustering Document Images Using Graph Summaries [chapter]

Eugen Barbu, Pierre Héroux, Sébastien Adam, Eric Trupin
2005 Lecture Notes in Computer Science  
Graph mining The main objective of graph mining is to provide new principles and efficient algorithms to mine topological substructures embedded in graph data" [5] .  ...  We describe a document using a bag of symbols found automatically using graph mining [5] techniques.  ... 
doi:10.1007/11510888_20 fatcat:sbeuldzgpjhfvkb5yuxwvhzhzq

Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity

Yi Jia, Jun Huan, Vincent Buhr, Jintao Zhang, Leonidas N Carayannopoulos
2009 BMC Bioinformatics  
Results: Here we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification.  ...  Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method.  ...  Graph database mining is an active research field in data mining research. The goal of graph database mining is to locate useful and interpretable patterns in a large volume of graph data.  ... 
doi:10.1186/1471-2105-10-s1-s46 pmid:19208148 pmcid:PMC2648771 fatcat:dq6cuvhpizf5zpdbl3hgmo2pdm

REAFUM: Representative Approximate Frequent Subgraph Mining [chapter]

Ruirui Li, Wei Wang
2015 Proceedings of the 2015 SIAM International Conference on Data Mining  
Noisy graph data and pattern variations are two thorny problems faced by mining frequent subgraphs.  ...  in the entire graph database; (2) then uses distinct patterns in the representative graphs as seed patterns to retrieve approximate matches in the entire graph database; (3) finally employs a consensus  ...  Gaston [23] adopts a step-wise approach using a combination of frequent paths, frequent free trees and cyclic graphs mining to discover all frequent subgraphs.  ... 
doi:10.1137/1.9781611974010.85 dblp:conf/sdm/LiW15 fatcat:aditlq6ghzc6xmiaoztarewxsq

Clustering document images using a bag of symbols representation

E. Barbu, P. Heroux, S. Adam, E. Trupin
2005 Eighth International Conference on Document Analysis and Recognition (ICDAR'05)  
Graph Mining "The main objective of graph mining is to provide new principles and efficient algorithms to mine topological substructures embedded in graph data" [16] .  ...  We describe a document using a bag of symbols found automatically using graph mining [16] techniques.  ... 
doi:10.1109/icdar.2005.75 dblp:conf/icdar/BarbuHAT05 fatcat:7zw3wkcyzbft3demaps4fhdat4


Jun Huan, Wei Wang, Jan Prins, Jiong Yang
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
In large graph databases, the total number of frequent subgraphs can become too large to allow a full enumeration using reasonable computational resources.  ...  Our method first mines all frequent trees from a general graph database and then reconstructs all maximal subgraphs from the mined trees.  ...  Our mining method is based on a novel graph mining framework in which we first mine all frequent tree patterns from a graph database and then construct maximal frequent subgraphs from trees.  ... 
doi:10.1145/1014052.1014123 dblp:conf/kdd/HuanWPY04 fatcat:ahjzbnbcafandoodi3f6kfxix4

Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining [article]

Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar
2021 arXiv   pre-print
Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed.  ...  Sequence feature embedding is a challenging task due to the unstructuredness of sequence, i.e., arbitrary strings of arbitrary length.  ...  Sequences dataset, = { % , … , ( } Sequence Graph Transform ⋮ ⋮ ⋮ … Ψ (-. ) Sequence Data Mining Ψ (-0 ) Ψ (-1 ) (b) Use of sequences' SGT embedding for data mining.  ... 
arXiv:1608.03533v15 fatcat:rnnyv76sq5fstj5zp5f5wlztoe

Frequent tree pattern mining: A survey

Aída Jiménez, Fernando Berzal, Juan-Carlos Cubero
2010 Intelligent Data Analysis  
The use of non-linear data structures is becoming more and more common in many data mining scenarios.  ...  We examine some of the most relevant tree mining algorithms and compare them in order to highlight their similarities and differences. A. Jiménez et al. / Frequent itemset mining: A survey Fig. 1.  ...  Pattern mining strategies Many frequent tree pattern mining algorithms have been proposed in the literature.  ... 
doi:10.3233/ida-2010-0443 fatcat:qyjf35zhijahdidz6rg4s7exiu

Structure feature selection for graph classification

Hongliang Fei, Jun Huan
2008 Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08  
In our method, we use frequent subgraphs as features for graph classification.  ...  With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning  ...  In this paper, we use frequent subgraph mining to extract features in a set of graphs. Each mined subgraph is a feature.  ... 
doi:10.1145/1458082.1458212 dblp:conf/cikm/FeiH08 fatcat:p2hs75zunrayfajkunyn6fe7je

Image Classification Using Subgraph Histogram Representation

Bahadir Ozdemir, Selim Aksoy
2010 2010 20th International Conference on Pattern Recognition  
Then, each graph is represented with a histogram of subgraphs selected using a frequent subgraph mining algorithm in the whole data.  ...  Using the subgraphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines  ...  The graph mining literature includes several approaches for frequent subgraph mining.  ... 
doi:10.1109/icpr.2010.278 dblp:conf/icpr/OzdemirA10 fatcat:smvazl7isjcfnmvtyr55hb7c34

GraphZip: Dictionary-based Compression for Mining Graph Streams [article]

Charles A. Packer, Lawrence B. Holder
2017 arXiv   pre-print
In this paper we present GraphZip, a scalable method for mining interesting patterns in graph streams.  ...  maximally-compressing patterns in a graph stream.  ...  Many graph mining algorithms aim to identify interesting patterns within an input graph.  ... 
arXiv:1703.08614v1 fatcat:qgy7votdxzgwlfckmoe5zqrzgq

Using Bags of Symbols for Automatic Indexing of Graphical Document Image Databases [chapter]

Eugen Barbu, Pierre Héroux, Sébastien Adam, Éric Trupin
2006 Lecture Notes in Computer Science  
Most of these use indexes depending on the tex-tual content of documents, and very few are able to handle graphical or image content without human annotation.  ...  Our approach uses data mining techniques for knowledge extraction. It aims at finding image parts that occur frequently in a given corpus.  ...  These frequent patterns are part of the document model and can be put in relation with the domain knowledge.  ... 
doi:10.1007/11767978_18 fatcat:y4ssebyeyvexxaqyozwan5ovji
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