Positive and negative association rule mining on XML data streams in database as a service concept

Çokpınar, Samet; Gündem, Taflan İmre

doi:10.1016/j.eswa.2012.01.128

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…The results surpass the ones which do not find the negative correlation. The other work provides the mining of positive and negative as a service (19). It works on XML data and the other work (20), concerned in mining the negative correlation in infrequent itemset.…”

Section: Related Workmentioning

confidence: 99%

Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

Wardani

2021

Baghdad Sci.J

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This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obtained rules from positive association rules and negative association rules strengthens to each other with a pretty good confidence score.

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Section: Related Workmentioning

confidence: 99%

Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

Wardani

2021

Baghdad Sci.J

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“…Subsequently, three 'association rules' techniques are further analysed. The reason for this selection is because they are the most well-known and used 'association rules' techniques (Cokpinar and Gundem, 2012). These are: Apriori Algorithm, Frequent Pattern (FP) Growth and Sampling algorithm.…”

Section: Association Rules and Spirometry Datamentioning

confidence: 99%

Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab

2014

Proceedings of the International Conference on Health Informatics

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An application of association rules mining method for the discovery of associations in abnormal quantitative health data for inadequately controlled severe allergic Asthma treated by Omazulimab is presented. To the best of authors' knowledge, no formal approaches have ever been used for extraction of association rules among dysfunctional elements in Spirometry datasets. Initially is provided an explanation of the procedures used for diagnosing inadequately controlled severe allergic Asthma. Following this, it is conducted critical evaluation of well-known 'association rule' mining techniques, in order to identify the one with the best utility for discovery of associations among abnormal elements of Spirometry datasets. Apriori Algorithm is applied to real-life Spirometry datasets to illustrate the contribution of application of association rule mining techniques. This revealed the existence of association rules among dysfunctional Spirometry elements for this disease. Moreover it has been identified that this disease is provoked by association of Spirometry elements that do not function properly as these are provided by Spirometer. This is translated in human factors as a dysfunction of small and medium airways of patients'. Furthermore Spirometry element FEV1, is not as valuable parameter as the European Medical Agency supports. Finally it has been observed that Omazulimab treatment improves respiratory function and makes the connection among associated elements weaker.

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“…(X → Y) is a negative association rule if the presence of X assures the absence of Y in the database. Many studies have been carried out to mine positive and negative association rules from different datasets [15][16][17][18]. Shaheen et al [7] introduced a variable called context that can essentially be used to mine valid positive and negative association rules.…”

Section: Introductionmentioning

confidence: 99%

CARM: Context Based Association Rule Mining for Conventional Data

Shaheen¹,

Abdullah²

2021

Computers, Materials &Amp; Continua

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This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatiotemporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country's energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.

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Positive and negative association rule mining on XML data streams in database as a service concept

Cited by 12 publications

References 15 publications

Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab

CARM: Context Based Association Rule Mining for Conventional Data

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