Mining association rules from structured XML data

Abazeed, Ashraf; Mamat, Ali; Nasir, M Nizlan; Ibrahim, Hamidah

doi:10.1109/iceei.2009.5254708

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Additionally, association rules can be generated from relational format [31], transactional databases [19,23] as well as from XML documents [26,27,[29][30][31]. From the critically reviewed literature on PPDM, a question can be raised "Which domain is not yet focused for privacy preservation of association rule mining?"…”

Section: Literature Reviewmentioning

confidence: 99%

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Hiding Sensitive XML Association Rules With Supervised Learning Technique

Iqbal¹,

Asghar²,

Mirza³

2011

IIM

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In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems

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Section: Literature Reviewmentioning

confidence: 99%

“…The performance of these algorithms is not compared and the security risks involved in the generation of association rules are ignored. Similarly, Abazeed et al [27] comes up with the modified version of FLEX (MFLEX) algorithm. This implementation is carried out by using DOM and SAX (simple API for XML).…”

Section: Literature Reviewmentioning

confidence: 99%

Hiding Sensitive XML Association Rules With Supervised Learning Technique

Iqbal¹,

Asghar²,

Mirza³

2011

IIM

View full text Add to dashboard Cite

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“…Efficient discovery of frequent patterns and association rules is the key contribution with the minimum communication cost. Abazeed et al [1] presented a modified version of FLEX algorithm (MFLEX) that uses java based parsers (DOM and SAX) for XML mining. The parser class reads the transaction through XML node reader and stores the transaction information into Linked-HashedMap structure.…”

Section: Xml Association Rule Miningmentioning

confidence: 99%

“…K2 algorithm discovers relationship among items/nodes in an incremental fashion by using a scoring function as presented in Eq. (1). The informal intuition of f (i, π i ) is the probability of XML database (X D ) given the parents of i th node/item are π i .…”

Section: Introductionmentioning

confidence: 99%

A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

Iqbal

Yin

Hao

et al. 2014

IDA

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The rationale of XML design is to transfer and store data at different levels. A key feature of these levels in an XML document is to identify its components for additional processing. XML components can expose sensitive information after application of data mining techniques over a shared database. Therefore, privacy preservation of sensitive information must be ensured prior to signify the outcome especially in sensitive XML Association Rules. Privacy issues in XML domain are not exceptionally addressed to determine a solution by the academia in a reliable and precise manner. In this paper, we have proposed a model for identifying sensitive items (nodes) to declare sensitive XML association rules and then to hide them. Bayesian networks-based central tendency measures are applied in declaration of sensitive XML association rules. K2 algorithm is used to generate Bayesian networks to ensure reliability and accuracy in preserving privacy of XML Association Rules. The proposed model is tested and compared using several case studies and large UCI machine learning datasets. The experimental results show improved accuracy and reliability of proposed model without any side effects such as new rules and lost rules. The proposed model uses the same minimum support threshold to find XML Association Rules from the original and transformed data sources. The significance of the proposed model is to minimize an incredible disclosure risk involved in XML association rule mining from external parties in a competitive business environment.

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An analytical review of XML association rules mining

Moradi

Keyvanpour

2013

Artif Intell Rev

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Mining association rules from structured XML data

Cited by 5 publications

References 6 publications

Hiding Sensitive XML Association Rules With Supervised Learning Technique

Hiding Sensitive XML Association Rules With Supervised Learning Technique

A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

An analytical review of XML association rules mining

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