Bottom-up discovery of frequent rooted unordered subtrees

Bei, Yijun; Chen, Gang; Shou, Lidan; Li, Xiaoyan; Dong, Jinxiang

doi:10.1016/j.ins.2008.08.020

Cited by 12 publications

(19 citation statements)

References 29 publications

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“…Table 1 summarizes some of the proposed approaches. Based on the type of tree, tree mining tasks can be classified as ordered (TreeMiner [87], FREQT [5], Chopper and XSpanner [79]) or unordered (Unot [6], uFreqt [60], [10]), rooted [10] or unrooted (CMTreeMiner [18], DRYADE [78]). Based on the type of subtree, they can be classified as induced [5], embedded [87,54], or bottom-up.…”

Section: Tree Miningmentioning

confidence: 99%

EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the wellknown maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees ---two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set. Disciplines Theory and AlgorithmsThis article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/11Noname manuscript No. The final publication is available at http://link.springer.com Abstract The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees -two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.

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Section: Tree Miningmentioning

confidence: 99%

EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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“…Another approach (Bei et al, 2009;Chen et al, 2006;Gu et al, 2007;Yang et al, 2008) of improving XML query performance is to discover frequent XML query patterns and to design an index mechanism or cache the results of these patterns. Bei et al (2009) and Yang et al (2008) design a transaction summary data structure (that is, the global tree) to merge all of XML user query patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Bei et al (2009) and Yang et al (2008) design a transaction summary data structure (that is, the global tree) to merge all of XML user query patterns. At the global tree, the XML candidate query sub trees are generated and their frequencies are thus counted by executing the tree-join process or database scans.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the frequent XML query patterns are efficiently discovered on the processed global tree. In addition, in order to reduce the number of XML candidate query sub trees, Bei et al (2009) and Yang et al (2008) use the minimum support constraint to prune the infrequent XML query patterns on the global tree.…”

Section: Introductionmentioning

confidence: 99%

“…The existing approaches (Bei et al, 2009;Chen et al, 2006;Gu et al, 2007;Yang et al, 2008) may not be suitable to discover the frequent XML query patterns in ebXML applications and thus, degrade the system performance. Bei et al (2009) and Yang et al (2008) generate the XML candidate query sub trees from the global tree and use costly containment testing to prune the invalid candidate ones for the queries.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient XML query pattern mining algorithm for ebXML applications in e-commerce

Chang¹

2014

Afr. J. Bus. Manage.

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Providing efficient query to XML data for ebXML applications in e-commerce is crucial, as XML has become the most important technique to exchange data over the Internet. ebXML is a set of specification for companies to exchange their data in e-commerce. Following the ebXML specifications, companies have a standard method to exchange business messages, communicate data, and business rules in e-commerce. Due to its tree-structure paradigm, XML is superior for its capability of storing and querying complex data for ebXML applications. Therefore, discovering frequent XML query patterns has become an interesting topic for XML data management in ebXML applications. The study presents an efficient mining algorithm, namely ebX 2 Miner, to discover the frequent XML query patterns for ebXML applications. Unlike the existing algorithms, the study proposes a new idea by encoding the XML user queries and then storing these codes to generate the frequent XML user query patterns. Furthermore, the simulation results show that the ebX 2 Miner outperforms other algorithms in its execution time and used memory space.

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An efficient algorithm of frequent XML query pattern mining for ebXML applications in e-commerce

Chang

Chen

2012

Expert Systems with Applications

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Bottom-up discovery of frequent rooted unordered subtrees

Cited by 12 publications

References 29 publications

EvoMiner: frequent subtree mining in phylogenetic databases

EvoMiner: frequent subtree mining in phylogenetic databases

An efficient XML query pattern mining algorithm for ebXML applications in e-commerce

An efficient algorithm of frequent XML query pattern mining for ebXML applications in e-commerce

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