Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets.

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Cytotoxicity Analysis of Nanoparticles by Association Rule Mining

Environmental Science Nano ◽

10.1039/d0en01240h ◽

2021 ◽

Author(s):

Gulsah Gul ◽

Ramazan Yildirim ◽

Nazar Ileri-Ercan

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Long Term Effects ◽

Rule Mining ◽

Efficient Delivery ◽

Intracellular Targets

Understanding the toxicity behavior of NPs is of great importance to ensure efficient delivery to intracellular targets without causing cytotoxicity, to measure the long-term effects of nanoparticles (NPs), and to...

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Security and privacy concerned association rule mining technique for the accurate frequent pattern identification

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.8908 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 19

Author(s):

T. Nusrat Jabeen ◽

M. Chidambaram ◽

G. Suseendran

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Frequent Pattern ◽

Security And Privacy ◽

Sensitive Information ◽

Simulation Environment ◽

Rule Mining ◽

Mining Technique ◽

Diffie Hellman

Security and privacy has emerged to be a serious concern in which the business professional don’t desire to share their classified transaction data. In the earlier work, secured sharing of transaction databases are carried out. The performance of those methods is enhanced further by bringing in Security and Privacy aware Large Database Association Rule Mining (SPLD-ARM) framework. Now the Improved Secured Association Rule Mining (ISARM) is introduced for the horizontal and vertical segmentation of huge database. Then k-Anonymization methods referred to as suppression and generalization based Anonymization method is employed for privacy guarantee. At last, Diffie-Hellman encryption algorithm is presented in order to safeguard the sensitive information and for the storage service provider to work on encrypted information. The Diffie-Hellman algorithm is utilized for increasing the quality of the system on the overall by the generation of the secured keys and thus the actual data is protected more efficiently. Realization of the newly introduced technique is conducted in the java simulation environment that reveals that the newly introduced technique accomplishes privacy in addition to security.

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A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.449 ◽

2010 ◽

Vol 39 ◽

pp. 449-454

Author(s):

Jiang Hui Cai ◽

Wen Jun Meng ◽

Zhi Mei Chen

Keyword(s):

Fault Diagnosis ◽

Association Rule ◽

Association Rule Mining ◽

Large Scale ◽

Predicate Logic ◽

Frequent Pattern ◽

Rule Mining ◽

First Order ◽

Broad Term ◽

Diagnosis Method

Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.

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Association Rule Mining III: Frequent Pattern Trees

Principles of Data Mining - Undergraduate Topics in Computer Science ◽

10.1007/978-1-4471-4884-5_18 ◽

2013 ◽

pp. 271-309

Author(s):

Max Bramer

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Frequent Pattern ◽

Rule Mining

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Mining Frequent Patterns Using Self-Organizing Map

Research and Trends in Data Mining Technologies and Applications ◽

10.4018/978-1-59904-271-8.ch005 ◽

2007 ◽

pp. 121-142 ◽

Cited By ~ 2

Author(s):

Fedja Hadzic ◽

Tharam Dillon ◽

Henry Tan ◽

Ling. Feng ◽

Elizabeth Chang

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Frequent Pattern ◽

Frequent Patterns ◽

Self Organizing Map ◽

Rule Mining ◽

Input Space ◽

Clustering Technique ◽

Downward Closure ◽

Self Organizing

Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step in association rule mining. Most of the proposed algorithms for extracting frequent patterns are based on the downward closure lemma concept utilizing the support and confidence framework. In this chapter we investigate an alternative method for mining frequent patterns in a transactional database. Self-Organizing Map (SOM) is an unsupervised neural network that effectively creates spatially organized internal representations of the features and abstractions detected in the input space. It is one of the most popular clustering techniques, and it reveals existing similarities in the input space by performing a topology-preserving mapping. These promising properties indicate that such a clustering technique can be used to detect frequent patterns in a top-down manner as opposed to the traditional approach that employs a bottom-up lattice search. Issues that are frequently raised when using clustering technique for the purpose of finding association rules are: (i) the completeness of association rule set, (ii) the support level for the rules generated, and (iii) the confidence level for the rules generated. We present some case studies analyzing the relationships between the SOM approach and the traditional association rule framework, and propose a way to constrain the clustering technique so that the traditional support constraint can be approximated. Throughout our experiments, we have demonstrated how a clustering approach can be used for discovering frequent patterns.

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Frequent Pattern Discovery and Association Rule Mining of XML Data

Data Mining ◽

10.4018/978-1-4666-2455-9.ch044 ◽

2013 ◽

pp. 859-879

Author(s):

Qin Ding ◽

Gnanasekaran Sundarraj

Keyword(s):

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Pattern Discovery ◽

Frequent Pattern ◽

Future Research ◽

Frequent Patterns ◽

Rule Mining ◽

Xml Data ◽

Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

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