Mining positive and Negative Association Rules from interesting frequent and infrequent itemsets

Swesi, Idheba Mohamad Ali O.; Bakar, Azuraliza Abu; Kadir, Anis Suhailis Abdul

doi:10.1109/fskd.2012.6234303

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…The two subsets must satisfy the conditions, A ∪ B = X and A ∩ B = . The two measures of an association rule A → B , confidence and correlation are, respectively, defined as 31 32 …”

Section: Methodsmentioning

confidence: 99%

Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

Yang

Chang

2015

Sci Rep

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Modeling the binding of transcription factors helps to decipher the control logic behind transcriptional regulatory networks. Position weight matrix is commonly used to describe a binding motif but assumes statistical independence between positions. Although current approaches take within-motif dependence into account for better predictive performance, these models usually rely on prior knowledge and incorporate simple positional dependence to describe binding motifs. The inability to take complex within-motif dependence into account may result in an incomplete representation of binding motifs. In this work, we applied association rule mining techniques and constructed models to explore within-motif dependence for transcription factors in Escherichia coli. Our models can reflect transcription factor-DNA recognition where the explored dependence correlates with the binding specificity. We also propose a graphical representation of the explored within-motif dependence to illustrate the final binding configurations. Understanding the binding configurations also enables us to fine-tune or design transcription factor binding sites, and we attempt to present the configurations through exploring within-motif dependence.

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“…The two subsets must satisfy the conditions, A ∪ B = X and A ∩ B = . The two measures of an association rule A → B , confidence and correlation are, respectively, defined as 31 32 …”

Section: Methodsmentioning

confidence: 99%

Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

Yang

Chang

2015

Sci Rep

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“…In addition, this study also uses the chi-squared test to measure the statistical significance of the association between the antecedent and the consequent. 14,[22][23][24][25] Support refers to the number of records where the attribute-value pairs in either set A or B appear in the dataset relative to the total number of records (transactions or instances), which indicates how frequently the itemset appears in the dataset. The Support value is symmetric so that Support (A !…”

Section: Association Rule Mining (Arm)mentioning

confidence: 99%

“…A high correlation points to a strong relationship between the two variables, while a low correlation means that the variables are weakly related. 14,[22][23][24][25] A chi-squared test is used in the analysis of contingency tables when the sample sizes are large. It is primarily used to examine whether two categorical variables are independent in influencing the test.…”

Section: Association Rule Mining (Arm)mentioning

confidence: 99%

The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining

Chaowchuen,

Warin,

Somyanonthanakul

et al. 2024

Eur J Dent

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Objective A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique. Materials and Methods This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017. The 5-year survival rate was assessed. The multivariable Cox proportional hazards analysis was performed to determine prognostic factors. ARM was applied to clinicopathologic and treatment modalities data to identify and rank the prognostic factors associated with oral cancer 5-year survival. Results The 5-year overall survival rate was 35.1%. Multivariable Cox proportional hazards analysis showed that tumor (T) stage, lymph node metastasis, surgical margin, extranodal extension, recurrence, and distant metastasis of tumor were significantly associated with overall survival rate (p < 0.05). The top associated death within 5 years rule was positive extranodal extension, followed by positive perineural and lymphovascular invasion, with confidence levels of 0.808, 0.808, and 0.804, respectively. Conclusion This study has shown that extranodal extension, and perineural and lymphovascular invasion were the top ranking and major deadly prognostic factors affecting the 5-year survival of oral cancer.

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“…Positive association rule mining has been implemented in the MapReduce environment by many [4,[18][19][20]27]. Few works have also been done on negative association rule mining [8,15,16,21,23,25,28], but none have been done in the big data framework. Since so much work has already been done on positive association rule mining over the years, in this related works section we are limiting our discussion to works that have used the less frequently addressed negative association rule mining.…”

Section: Related Workmentioning

confidence: 99%

Positive and negative association rule mining in Hadoop’s MapReduce environment

Bagui

Dhar

2019

J Big Data

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Association rule mining, originally developed by [3], is a well-known data mining technique used to find associations between items or itemsets. In today's big data environment, association rule mining has to be extended to big data. The Apriori algorithm is one of the most commonly used algorithms for association rule mining [4]. Using the Apriori algorithm, we find frequent patterns, that is, patterns that occur frequently in data. The Apriori algorithm employs an iterative approach where k-itemsets are used to explore (k + 1) itemsets. To find the frequent itemsets, first the set of frequent 1-itemsets are found by scanning the database and accumulating their counts. Itemsets that satisfy the minimum support threshold are kept. These are then used to find the frequent 2-itemsets. This process goes on until the newly generated itemset is an empty set, that is, until there are no more itemsets that meet the minimum support threshold. Then the itemsets are checked against a minimum confidence level to determine the association rules. The process of generating the frequent itemsets calls for repeated full scans of the database, and in this era of big data, this is a major challenge of this algorithm. Figure 1 presents a flow chart of how the Apriori algorithm works. Traditional association rule mining algorithms, like Apriori, mostly mine positive association rules. Positive association rule mining finds items that are positively related to one another, that is, if one item goes up, the related item also goes up. Though the classic application of positive association rule mining is market basket analysis, applications of

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Mining positive and Negative Association Rules from interesting frequent and infrequent itemsets

Cited by 22 publications

References 10 publications

Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining

Positive and negative association rule mining in Hadoop’s MapReduce environment

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