Positive and Negative Association Rules Mining for Mental Health Analysis of College Students

Zhao, Long; Feng, Hao; Xu, Tiantian; Xiang, Dong

doi:10.12973/eurasia.2017.01011a

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Association rule mining is one of the most popular data mining processes. The relationship rule is used to correlate two or more sets of data within larger data groups [26] using several algorithms. For example, market basket analysis is used to find product relationships in customers who tend to buy when a promotional campaign is run based on correlation rules, the percentage of confidence, and the support costs incurred.…”

Section: Association Rule Miningmentioning

confidence: 99%

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

Sawangarreerak

Thanathamathee

2021

Journal of Open Innovation: Technology, Market, and Complexity

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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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Section: Association Rule Miningmentioning

confidence: 99%

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

Sawangarreerak

Thanathamathee

2021

Journal of Open Innovation: Technology, Market, and Complexity

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“…ARM is an evolving research area and various algorithms have been developed for the genera-tion of strong interesting association rules (ARs) in large datasets. The prominent algorithms in-clude the positive AR (Agrawal et al, 1993;Bar-alis et al, 2008;Krishnapuram, 2016), negative AR (Balakrishna et al, 2019;Jabbour et al, 2018;Kong et al, 2018), and combined approach for neg-ative and positive rules for large datasets (Bagui & Dhar, 2018;Bemarisika & Totohasina, 2018;Par-fait et al, 2018;Zhao et al, 2017). Moreover, ap-plication specific ARM algorithms have been de-veloped for various areas including medicine (Bo-rah & Nath, 2018;Harahap et al, 2018;Moses et al, 2015), crime (Buczak & Gifford, 2010;Has-sani et al, 2016), agriculture (Bhatia & Gupta, 2014;Bisht & Samantaray, 2015;Geetha, 2015), distributed environments (Qin et al, 2016;Salah et al, 2017), data warehousing (Usman, 2017) etc.…”

Section: Related Workmentioning

confidence: 99%

Mining predicate rules without minimum support threshold

Ahmad

Sim

Shah

et al. 2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

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“…In addition, There are many machine-learning algorithms used in the application of mental health issues [39], such as the association rules [40,41], that present a method to detect depression from collected Time-series mobile data via association rule mining. As well, the search work in [42] provides a review of the latest works on depression detection systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Al-Hagery¹,

Alfaozan²,

Alghofaily³

et al. 2020

IJITCS

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Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five classes and the most affected age group in depression was in the age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.

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Positive and Negative Association Rules Mining for Mental Health Analysis of College Students

Cited by 10 publications

References 10 publications

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

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

Mining predicate rules without minimum support threshold

Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

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