MapReduce Based Multilevel Association Rule Mining from Concept Hierarchical Sales Data

Prajapati, Dinesh J.; Garg, Sanjay

doi:10.1007/978-981-10-5427-3_64

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…It is a powerful tool to uncover intricate patterns and interconnections among diverse factors influencing crop yields, soil health, and overall sustainability. Notably, some researchers have undertaken investigations into association rules linking soil types and crop types, utilizing the A‐priori algorithm (Prajapati & Kathiriya, 2016). To harness the capabilities of ARM effectively, it is imperative to address the quantitative nature of agricultural data, environmental covariates, and soil attributes.…”

Section: Related Workmentioning

confidence: 99%

A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region

Belkadi,

Drias,

Drias

et al. 2023

Expert Systems

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Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture.

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Section: Related Workmentioning

confidence: 99%

A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region

Belkadi,

Drias,

Drias

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…For example, Kiran et al [45] proposed a hierarchical clustering algorithm using closed frequent itemsets that use Wikipedia as an external knowledge to enhance the document representation. In Prajapati and Garg's research [46], the transactional dataset is generated from a big sales dataset; then, the distributed multilevel frequent pattern mining algorithm (DMFPM) is implemented to generate level-crossing frequent itemset using the Hadoop Mapreduce framework. And then, the multilevel association rules are generated from frequent itemset.…”

Section: Sequential Pattern Mining With Hierarchical Relationmentioning

confidence: 99%

An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

Zhang

2020

Wireless Communications and Mobile Computing

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High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (s-sequences) as high-utility hierarchical sequential patterns from g-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.

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MapReduce Based Multilevel Association Rule Mining from Concept Hierarchical Sales Data

Cited by 2 publications

References 15 publications

A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region

A SCORPAN‐based data warehouse for digital soil mapping and association rule mining in support of sustainable agriculture and climate change analysis in the Maghreb region

An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

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