A Framework for Mining Association Rules in Data Warehouses

Tjioe, Haorianto Cokrowijoyo; Taniar, David

doi:10.1007/978-3-540-28651-6_23

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Mining multidimensional data need additional attention in terms of mining algorithms and tools. Taniar et al [36] and Tjioe and Taniar [37] propose association mining rules to data warehouse scenarios. Nimmagadda and Dreher [27] propose classical mining algorithms for analyzing the metadata views.…”

Section: Data Characterization and Miningmentioning

confidence: 99%

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Nimmagadda

Dreher

Rudra

2016

Environ Model Assess

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Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO 2 ) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust Bontology-based multidimensional data warehousing and mining^approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes' correlations, patterns, and trends that can help in predicting future forecast of CO 2 emissions worldwide.

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Section: Data Characterization and Miningmentioning

confidence: 99%

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Nimmagadda

Dreher

Rudra

2016

Environ Model Assess

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“…Now major difference between the frequent pattern-growth and other algorithms is found out to be that FP-growth does not essentially generate the candidates, rather it just tests them. Whereas the Apriori algorithm not only just tests the candidate item sets but beforehand generates them also [9]. The motivation behind FP-tree methods has been the following:…”

Section: Fp-growthmentioning

confidence: 99%

FP-Growth Implementation Using Tries for Association Rule Mining

Goel

2017

Advances in Intelligent Systems and Computing

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Abstract. With the advent of technology the past few years have seen the rise in the field of data mining. Data mining generally considered as the process of extracting the useful information by finding the hidden and the non-trivial information out of large chunks of dataset. Now here comes the role of association rule mining which forms a crucial component of data mining. Many recent applications like market basket analysis, text mining etc. are done using this approach. In this paper we have discussed the novel approach to implement the FP Growth method using the trie data structure. Tries provide a special feature of merging the shared sets of data with the number of occurrences that were already registered as count. So this paper widely gives an idea about how the interesting patterns are generated from the large databases using association rule mining methodologies.

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Extending the UML for Designing Association Rule Mining Models for Data Warehouses

Zubcoff

Trujillo

2005

Data Warehousing and Knowledge Discovery

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A Framework for Mining Association Rules in Data Warehouses

Cited by 5 publications

References 6 publications

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

FP-Growth Implementation Using Tries for Association Rule Mining

Extending the UML for Designing Association Rule Mining Models for Data Warehouses

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