A recursive partitioning algorithm for space information flow

2014 ◽  
Author(s):  
Jiaqing Huang ◽  
Zongpeng Li
Keyword(s):  
Information Flow ◽  
Recursive Partitioning ◽  
Partitioning Algorithm

From Bean Counters to Bean Growers: Accountants as Data Analysts—A Customer Profitability Example

2015 ◽  
Vol 29 (3) ◽  
pp. 151-164 ◽  
Author(s):  
Matthew D. Pickard ◽  
Gary Cokins
Keyword(s):  
Data Mining ◽  
Recursive Partitioning ◽  
Quality Data ◽  
Customer Profitability ◽  
Data Mining Technique ◽  
Binary Decision ◽  
Mining Technique ◽  
Binary Decision Tree ◽  
Strategic Role ◽  
Partitioning Algorithm

ABSTRACT Extensive data mining and analytics (DM&A) are increasingly requisite for companies to be competitive in this age of information. This demand, combined with (1) accountants' reputation for understanding and generating quality data, and (2) the increased accessibility of DM&A tools, has created a unique opportunity for accountants to play a larger strategic role in their organization. We argue that accountants should own and drive a larger part of the DM&A that occurs in their organization. To support this vision, we introduce a data mining technique called recursive partitioning. We illustrate how it can be applied to a large customer costing and profit dataset to identify the characteristics that differentiate more and less profitable customers. We discuss how the output of the recursive partitioning algorithm (a binary decision tree) can be used to increase customer profitability and identify future profitable customers. We conclude by suggesting and discussing some of the obstacles and research opportunities that this vision presents to the accounting field.

scholarly journals Identification of Information Leakage and Guilt Agent by using MAC-IP Binding and Recursive Partitioning Algorithm to Modulate the Uncertainty in the Organization’s Network

2019 ◽  
Vol 9 (1) ◽  
pp. 4091-4096
Keyword(s):  
Decision Tree ◽  
Recursive Partitioning ◽  
Information Leakage ◽  
Third Party ◽  
Sensitive Information ◽  
Sensitive Data ◽  
The Third ◽  
Partitioning Algorithm ◽  
The Third Party ◽  
Modern Era

In this modern era, all organizations depend on internet and data so, maintaining of all data is done by the third party in large organizations. But in this present on-developing world, one have to share the data inside or outside the organization which incorporates the sensitive data of the venture moreover. Data of the organization have sensitive data which should not share with any others but unfortunately, that data was there in the third party hands so; we need to protect the data and also have to identify the guilt agent. For this, we propose a model that would evaluate and correctly identifies guilt agents, for which a recursive partitioning has been created which is a decision tree that spills data in to the sub partitions and does the easiest way to get alert and at least one specialist or it can autonomously accumulate by some different means. The main intention of the model is to secure sensitive information by recognizing the leakage and distinguish the guilt agent.

scholarly journals Application of Recursive Partitioning to Agricultural Credit Scoring

1999 ◽  
Vol 31 (1) ◽  
pp. 109-122 ◽  
Author(s):  
Michael P. Novak ◽  
Eddy LaDue
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Recursive Partitioning ◽  
Agricultural Credit ◽  
Misclassification Costs ◽  
Out Of Sample ◽  
Partitioning Algorithm ◽  
Direct Incorporation

AbstractRecursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.

scholarly journals PENGARUH FAKTOR KLIEN DAN FAKTOR AUDITOR TERHAD AP AUDITOR CHANGES Sebuah Pendekatan dengan Model Kontijensi RPA (Recursive Model Alogarithm)

2017 ◽  
Vol 3 (2) ◽  
pp. 133
Author(s):  
Aida Ainul Mardiyah
Keyword(s):  
Regression Analysis ◽  
Response Bias ◽  
Random Sampling ◽  
Recursive Partitioning ◽  
Mail Survey ◽  
The Other ◽  
Related Factors ◽  
Dependency Relationship ◽  
Study Results ◽  
Partitioning Algorithm

<p class="Style4">This study intends to identify the effects of client-related factors and auditor-related factors on the auditor changes. Data is selected using random sampling and purposive sampling. The data collection is performed using mail survey and archaival. The statistic method used to test the hypotheses is regression analysis and RPA (Re-cursive Partitioning Algorithm) model.</p><p class="Style4">The study results are as follows: first, The results provide support for the hypothesis that ctient-related factors and auditor-related factors on the auditor changes; second, the normal data test and non response bias using t-<sub>ba</sub> shows an insignificant result This means that there are non response bias and the normal data; third, this is demonstrated by the multicolinearity number r&lt; 0,8 or VIF mean 1 that shows that the multicolinearity is not dangerous, the Durbin Watson approaches 2 and BG (The Breussh-Godfrey) r = 0 which means that between one variable and the other there is no dependency relationship (independent), and and homoscedacity occur.</p><p class="Style3"><em>Keywords: RPA (Recursive Partitioning Algorithm), client-related factors, auditor-related factors and auditor changes</em></p>

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