AMIOT: Induced Ordered Tree Mining in Tree-Structured Databases

2006 ◽  
Author(s):  
S. Hido ◽  
H. Kawano
Keyword(s):  
Tree Mining ◽  
Ordered Tree

scholarly journals Limiting Until in Ordered Tree Query Languages

2016 ◽  
Vol 17 (2) ◽  
pp. 1-34
Author(s):  
Michael Benedikt ◽  
Clemens Ley
Keyword(s):  
Query Languages ◽  
Ordered Tree

scholarly journals CASA: An Efficient Automated Assignment of Protein Mainchain NMR Data Using an Ordered Tree Search Algorithm

2005 ◽  
Vol 33 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Jianyong Wang ◽  
Tianzhi Wang ◽  
Erik R. P. Zuiderweg ◽  
Gordon M. Crippen
Keyword(s):  
Search Algorithm ◽  
Automated Assignment ◽  
Tree Search ◽  
Nmr Data ◽  
Ordered Tree ◽  
Tree Search Algorithm

RP-Tree: Rare Pattern Tree Mining

2011 ◽  
pp. 277-288 ◽  
Author(s):  
Sidney Tsang ◽  
Yun Sing Koh ◽  
Gillian Dobbie
Keyword(s):  
Tree Mining

Incorporating Qualitative Information for Credit Risk Assessment through Frequent Subtree Mining for XML

2013 ◽  
pp. 452-489
Author(s):  
Novita Ikasari ◽  
Fedja Hadzic ◽  
Tharam S. Dillon
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Grameen Bank ◽  
Distinctive Character ◽  
Credit Risk Assessment ◽  
Tree Mining ◽  
Data Objects ◽  
Frequent Subtree Mining

Credit risk assessment has been one of the most appealing topics in banking and finance studies, attracting both scholars’ and practitioners’ attention for some time. Following the success of the Grameen Bank, works on credit risk, in particular for Small Medium Enterprises (SMEs), have become essential. The distinctive character of SMEs requires a method that takes into account quantitative and qualitative information for loan granting decision purposes. In this chapter, we first provide a survey of existing credit risk assessment methods, which shows a current gap in the existing research in regards to taking qualitative information into account during the data mining process. To address this shortcoming, we propose a framework that utilizes an XML-based template to capture both qualitative and quantitative information in this domain. By representing this information in a domain-oriented way, the potential knowledge that can be discovered for evidence-based decision support will be maximized. An XML document can be effectively represented as a rooted ordered labelled tree and a number of tree mining methods exist that enable the efficient discovery of associations among tree-structured data objects, taking both the content and structure into account. The guidelines for correct and effective application of such methods are provided in order to gain detailed insight into the information governing the decision making process. We have obtained a number of textual reports from the banks regarding the information collected from SMEs during the credit application/evaluation process. These are used as the basis for generating a synthetic XML database that partially reflects real-world scenarios. A tree mining method is applied to this data to demonstrate the potential of the proposed method for credit risk assessment.

Incorporating Qualitative Information for Credit Risk Assessment through Frequent Subtree Mining for XML

2011 ◽  
pp. 467-503
Author(s):  
Novita Ikasari ◽  
Fedja Hadzic ◽  
Tharam S. Dillon
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Grameen Bank ◽  
Distinctive Character ◽  
Credit Risk Assessment ◽  
Tree Mining ◽  
Data Objects ◽  
Frequent Subtree Mining

Credit risk assessment has been one of the most appealing topics in banking and finance studies, attracting both scholars’ and practitioners’ attention for some time. Following the success of the Grameen Bank, works on credit risk, in particular for Small Medium Enterprises (SMEs), have become essential. The distinctive character of SMEs requires a method that takes into account quantitative and qualitative information for loan granting decision purposes. In this chapter, we first provide a survey of existing credit risk assessment methods, which shows a current gap in the existing research in regards to taking qualitative information into account during the data mining process. To address this shortcoming, we propose a framework that utilizes an XML-based template to capture both qualitative and quantitative information in this domain. By representing this information in a domain-oriented way, the potential knowledge that can be discovered for evidence-based decision support will be maximized. An XML document can be effectively represented as a rooted ordered labelled tree and a number of tree mining methods exist that enable the efficient discovery of associations among tree-structured data objects, taking both the content and structure into account. The guidelines for correct and effective application of such methods are provided in order to gain detailed insight into the information governing the decision making process. We have obtained a number of textual reports from the banks regarding the information collected from SMEs during the credit application/evaluation process. These are used as the basis for generating a synthetic XML database that partially reflects real-world scenarios. A tree mining method is applied to this data to demonstrate the potential of the proposed method for credit risk assessment.

Impact of parallel heterogeneity on a continuum model of the pulmonary arterial tree

1994 ◽  
Vol 77 (2) ◽  
pp. 660-670 ◽  
Author(s):  
G. S. Krenz ◽  
J. Lin ◽  
C. A. Dawson ◽  
J. H. Linehan
Keyword(s):  
Continuum Model ◽  
Vessel Diameter ◽  
Arterial Tree ◽  
Vascular Volume ◽  
Homogeneous Trees ◽  
Number Ratio ◽  
Pulmonary Arterial ◽  
Beta 2 ◽  
Ordered Tree ◽  
Intravascular Pressure

Model arterial trees were constructed following rules consistent with morphometric data, Nj = (Dj/Da)-beta 1 and Lj = La(Dj/Da)beta 2, where Nj, Dj, and Lj are number, diameter, and length, respectively, of vessels in the jth level; Da and La are diameter and length, respectively, of the inlet artery, and -beta 1 and beta 2 are power law slopes relating vessel number and length, respectively, to vessel diameter. Simulated heterogeneous trees approximating these rules were constructed by assigning vessel diameters Dm = Da[2/(m + 1)]1/beta 1, such that m-1 vessels were larger than Dm (vessel length proportional to diameter). Vessels were connected, forming random bifurcating trees. Longitudinal intravascular pressure [P(Qcum)] with respect to cumulative vascular volume [Qcum] was computed for Poiseuille flow. Strahler-ordered tree morphometry yielded estimates of La, Da, beta 1, beta 2, and mean number ratio (B); B is defined by Nk + 1 = Bk, where k is total number of Strahler orders minus Strahler order number. The parameters were used in P(Qcum) = Pa [formula: see text] and the resulting P(Qcum) relationship was compared with that of the simulated tree, where Pa is total arterial pressure drop, Q is flow rate, Ra = (128 microLa)/(pi D4a (where mu is blood viscosity), and Qa (volume of inlet artery) = 1/4D2a pi La. Results indicate that the equation, originally developed for homogeneous trees (J. Appl. Physiol. 72: 2225–2237, 1992), provides a good approximation to the heterogeneous tree P(Qcum).

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