Data Mining in Frequent Pattern Matching Using Improved Apriori Algorithm

Krishna, Bhukya; Amarawat, Geetanjali

doi:10.1007/978-981-13-1498-8_61

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Krishna and Amarawat [13] proposed a sampling-based approach to improve the performance of the Apriori algorithm. The algorithm utilizes random sampling from the database to identify frequent itemsets.…”

Section: E Samplingmentioning

confidence: 99%

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Herath,

Wijenayake

2024

CONTRE

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Data mining is the process of obtaining valuable or significant information from a large-scale database. One significantarea of research in the field of data mining is association rules mining. Apriori algorithm is one of the classical algorithmsin the association rule mining field. This research analyses the basic ideas and shortcomings of the Apriori algorithm andcompares several different styles of its major improvement strategies. Then it suggests an improved version of the Apriorialgorithm that utilizes a dataset summarization method, an optimized database mapping technique, an intersection operation,and a joined optimization strategy. These enhancements aim to address the low performance and efficiency by reducing thegeneration of candidate itemsets and minimizing the execution time. This addresses the issues of generating numerous candidate itemsets and repeatedly scanning the transaction database. After implementing the optimized algorithm, to verify its effectiveness, it has been applied to a groceries dataset, which is for market basket analysis. The improved Apriori algorithm demonstrated significant enhancements over the original algorithm in terms of reduced candidate itemsets and running time, leading to improved algorithm efficiency.

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Section: E Samplingmentioning

confidence: 99%

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Herath,

Wijenayake

2024

CONTRE

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“…In an Index-Tree, there may be several nodes with count zero. Such nodes not only waste disk space but also degrade the performance of the algorithm of mining frequent intervals [13,14]. Therefore we do not create such nodes in an Index-Tree.…”

Section: Index-tree Definitionmentioning

confidence: 99%

Temporal association rules discovery algorithm based on improved index tree

Chen

Wang

Zeng

et al. 2021

Applied Mathematics and Nonlinear Sciences

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With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

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“…Two steps are included in the process of the association rule: finding frequent items and association rules [57]. The first step is a comparison between items and support threshold S. All items obtained by scanning the database are compare with S to represent frequent items, which are used for finding associations rules in the next step.…”

Section: ) Completed Svm-latticementioning

confidence: 99%

SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles

Yang,

Qu,

Cai

et al. 2020

Preprint

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In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them. The DoPS, our previous work [2], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods.

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Data Mining in Frequent Pattern Matching Using Improved Apriori Algorithm

Cited by 9 publications

References 5 publications

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Temporal association rules discovery algorithm based on improved index tree

SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles

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