Clustering Based Approach to Enhance Association Rule Mining

Kanhere, Samruddhi; Sahni, Anu; Stynes, Paul

doi:10.23919/fruct50888.2021.9347577

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…FP-growth is a way to look for a model of data often occurring which is an algorithm in data association without creating candidate itemset [20], [21]. Its stage's function is to use the FP-tree structure stating from reading data in order to create the tree and them detect the in-depth data to find the data appearing together frequently.…”

Section: Frequent Pattern (Fp) Growth Algorithm In Data Miningmentioning

confidence: 99%

Analysis of clustering and association using data mining technique for elderly health condition dataset

Panthong,

Wongkanthiya

2023

IJ-AI

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Data survey on the elderly health condition in each year aimed to investigate the performance result on the elderly health care and to evaluate the elderly’s health and health promotion. Thus, in analyzing the data, it mainly relied on the mining data technique for the evaluating health condition. This study presented the data analysis by clustering method. Then, the data was taken from each group to find the association rule. The analysis results showed that the elderly’s health condition data could be classified into four different groups; cluster 1 (25%) were male elderly with high blood pressure and smoking cigarette, cluster 2 (25%) were female elderly with no the congenital disease but the result from the eye sight examination, it was found that they were long-sighted, cluster 3 (24%) were female elderly with no the congenital disease but having the insomnia and osteoarthritis and cluster 4 (26%) were female elderly with high blood pressure and diabetes. It also indicated that each group had the rule showing the correlation between the data in each group having the minimum value of confidence at 0.8 and the minimum value of support not less than 0.5.

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Section: Frequent Pattern (Fp) Growth Algorithm In Data Miningmentioning

confidence: 99%

Analysis of clustering and association using data mining technique for elderly health condition dataset

Panthong,

Wongkanthiya

2023

IJ-AI

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“…Appropriate product recommendations can help in marketing strategies, especially product promotion and production planning and helps in making decisions regarding product stock [27]. The clustering-based approach used not only consists of items that appear frequently but also considers their contribution to overall income by considering their prices [28].…”

Section: Second Section: K-means Algorithmmentioning

confidence: 99%

Data mining technique for grouping products using clustering based on association

Mandala

Putri

2023

IJEECS

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There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.

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“…Unsupervised learning models (clustering models), which are adept in separating and gathering data from one another, have thus been suggested as alternatives to traditional approaches for extracting useful knowledge. However, when used with highdimensional data description spaces, clustering models can also result in imprecise knowledge [5]. A multi-view approach that splits the description space of data into several subspaces, a subspace is known as a view, has been proposed to address this problem [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views

Shehabi

İmamoğlu

2023

International Journal of Computational and Experimental Science and Engineering

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Data mining involves examining vast quantities of data to uncover valuable insights that can be utilized for making informed decisions and driving business objectives. The study focuses on the task of finding relationships between features belonging to two different views using multi-view model, and proposes a novel approach called MARCMV. This approach extracts multi-view association rules from different views of the same data set using multi-clustering neural model. The study finds that MARCMV outperforms conventional symbolic methods in terms of association rule quality and running time.

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Clustering Based Approach to Enhance Association Rule Mining

Cited by 8 publications

References 18 publications

Analysis of clustering and association using data mining technique for elderly health condition dataset

Analysis of clustering and association using data mining technique for elderly health condition dataset

Data mining technique for grouping products using clustering based on association

MARCMV: Mining Multi-View Association Rules from Clustered Multi-Views

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