Recognition of Largest Common Structural Fragment among a Variety of Chemical Structures

Takahashi, Yoshimasa; Satoh, Yuzuru; Suzuki, Hajime; Sasaki, Shin‐ichi

doi:10.2116/analsci.3.23

Cited by 50 publications

(21 citation statements)

References 12 publications

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“…For example, mining frequent substructures and subgraph patterns from many graphs (i.e., a graph database) has been studied intensively [4,27,16,17,20,32,31,28]. Yan et al [33] used frequent graph patterns to index graphs.…”

Section: Related Workmentioning

confidence: 99%

On mining cross-graph quasi-cliques

Pei

Jiang

Zhang

2005

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining

202

124

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Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasicliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.

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Section: Related Workmentioning

confidence: 99%

On mining cross-graph quasi-cliques

Pei

Jiang

Zhang

2005

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining

202

124

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show abstract

“…Though there is a previous definition of LCS between trees more related to isomorphism [21], we define it differently, in a consistent way to the general definition.…”

Section: Largest Common Subforest (Lcsforest)mentioning

confidence: 99%

Generalized LCS

Amir

Hartman

Kapah

et al. 2008

Theoretical Computer Science

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a b s t r a c tThe Longest Common Subsequence (LCS) is a well studied problem, having a wide range of implementations. Its motivation is in comparing strings. It has long been of interest to devise a similar measure for comparing higher dimensional objects, and more complex structures. In this paper we study the Longest Common Substructure of two matrices and show that this problem is N P -hard. We also study the Longest Common Subforest problem for multiple trees including a constrained version, as well. We show N P -hardness for k > 2 unordered trees in the constrained LCS. We also give polynomial time algorithms for ordered trees and prove a lower bound for any decomposition strategy for k trees.

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“…For example, [1,7] study the the problem of finding common substructures from chemical compounds. SUBDUE [4] proposes an approximate algorithm to identify some, instead of the complete set of, frequent substructures.…”

Section: Related Workmentioning

confidence: 99%

Scalable mining of large disk-based graph databases

Wang

Pei

et al. 2004

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Mining frequent structural patterns from graph databases is an interesting problem with broad applications. Most of the previous studies focus on pruning unfruitful search subspaces effectively, but few of them address the mining on large, disk-based databases. As many graph databases in applications cannot be held into main memory, scalable mining of large, disk-based graph databases remains a challenging problem. In this paper, we develop an effective index structure, ADI (for adjacency index), to support mining various graph patterns over large databases that cannot be held into main memory. The index is simple and efficient to build. Moreover, the new index structure can be easily adopted in various existing graph pattern mining algorithms. As an example, we adapt the well-known gSpan algorithm by using the ADI structure. The experimental results show that the new index structure enables the scalable graph pattern mining over large databases. In one set of the experiments, the new disk-based method can mine graph databases with one million graphs, while the original gSpan algorithm can only handle databases of up to 300 thousand graphs. Moreover, our new method is faster than gSpan when both can run in main memory.

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Recognition of Largest Common Structural Fragment among a Variety of Chemical Structures

Cited by 50 publications

References 12 publications

On mining cross-graph quasi-cliques

On mining cross-graph quasi-cliques

Generalized LCS

Scalable mining of large disk-based graph databases

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