An Improved Association Rule Mining Technique for Xml Data Using Xquery and Apriori Algorithm

Porkodi, R; Bhuvaneswari, V.; Rajesh, R.; Amudha, T.

doi:10.1109/iadcc.2009.4809242

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. So, the algorithm for finding association dependencies is well-solved with MapReduce [16,17].…”

Section: Resultsmentioning

confidence: 99%

Association Rules Mining in Big Data

Shakhovska¹,

Kaminskyy²,

Zasoba³

et al. 2018

IJC

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The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.

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Section: Resultsmentioning

confidence: 99%

Association Rules Mining in Big Data

Shakhovska¹,

Kaminskyy²,

Zasoba³

et al. 2018

IJC

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“…It can provide high quality and actionable information for the innovative or customised bundling of the products and services. The importance of an association rule can be evaluated by support, confidence, and lift (Porkodi et al , 2009; Qodmanan et al , 2011). In a large transaction database, the managers are usually only interested in the items that are frequently purchased together (Agrawal et al , 1993).…”

Section: Methodsmentioning

confidence: 99%

Analysing customer behaviour in mobile app usage

Chen

Zhang

Zhao

2017

IMDS

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Purpose – Big data produced by mobile apps contains valuable knowledge about customers and markets and has been viewed as productive resources. This study proposes a multiple methods approach to elicit intelligence and value from big data by analysing customer behaviour in mobile app usage. Design/methodology/approach – The big data analytical approach is developed using three data mining techniques: RFM (Recency, Frequency, Monetary) analysis, link analysis, and association rule learning. We then conduct a case study to apply the approach to analyse the transaction data extracted from a mobile app. Findings – The approach can identify high-value and mass customers, and understand their patterns and preferences in using the functions of the mobile app. Such knowledge enables the developer to capture the behaviour of large pools of customers and to improve products and services by mixing and matching functions and offering personalised promotions and marketing information. Originality/value – The approach used in this study balances complexity with usability, thus facilitating corporate use of big data in making product improvement and customisation decisions. The approach allows developers to gain insights into customer behaviour and function usage preferences by analysing big data. The identified associations between functions can also help developers improve existing, and design new, products and services to satisfy customers’ unfulfilled requirement

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“…Porkodi R. et al [15] have presented an improved framework for mining association rules from XML data using XQUERY and. NET based implementation of Apriori algorithm.…”

Section: Review Of Related Workmentioning

confidence: 99%

Mining association rules from XML document using modified index table

Sasikala¹,

Premalatha²

2013

2013 International Conference on Computer Communication and Informatics

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In recent years the amount of unstructured databases increased drastically. In lieu of continuous increasing of XML databases, it is mandatory to propose a method to retrieve useful information. Association Rule Mining places a major role to retrieve this useful information. XML data bases have more challenges because of its inherent flexibility in structure and semantics. Many methods were suggested to mine association rules from these databases. In this paper a modified index table method is proposed to mine the interesting relationship among the XML documents. To accomplish the process more practical and computationally good, a modified index table is introduced. To show the effectiveness of this method a set of experiments are conducted on REED dataset and the results are discussed.

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An Improved Association Rule Mining Technique for Xml Data Using Xquery and Apriori Algorithm

Cited by 13 publications

References 3 publications

Association Rules Mining in Big Data

Association Rules Mining in Big Data

Analysing customer behaviour in mobile app usage

Mining association rules from XML document using modified index table

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