Classification of association rules based on K-means algorithm

Dahbi, Azzeddine; Mouhir, Mohamed; Balouki, Youssef; Gadi, Taoufiq

doi:10.1109/cist.2016.7805061

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…In [26], researchers have proposed a new method to cluster the association rules by K-means (partitional) clustering algorithm. The main goal of this research is the clustering of discovered association rules to make it easy for users to choose the best rules.…”

Section: Related Workmentioning

confidence: 99%

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Mattiev

Kavšek

2021

ComSIS

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Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rule discovered on those ?big? datasets can easily exceed thousands. To produce compact, understandable and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. In this paper, we propose new methods that are able to reduce the number of class association rules produced by ?classical? class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose new associative classifiers, called DC, DDC and CDC, that use distance-based agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules, and in the rule-selection step, we use different strategies (based on database coverage and cluster center) for each algorithm. Experimental results performed on selected datasets from the UCI ML repository show that our classifiers are able to learn classifiers containing significantly fewer rules than state-of-the-art rule learning algorithms on datasets with a larger number of examples. On the other hand, the classification accuracy of the proposed classifiers is not significantly different from state-of-the-art rule-learners on most of the datasets.

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Section: Related Workmentioning

confidence: 99%

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Mattiev

Kavšek

2021

ComSIS

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“…In this study, a clustering dataset with the K-means algorithm was conducted. Clustering is an unsupervised classification method that produces distinct objects, known as disjoint clusters, based on one or more similarity criteria [7]. The most well-known clustering algorithm is K-Means [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis and design of quality control system in ground coffee SMEs

Cendikia

Raharja

Udin

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Demands for quality assurance of food products are increasing due to the implementation of free trade. These demands require improvements in the application of food quality control process. SMEs in Indonesia inspect the quality of ground coffee manually, the subjectivity and long inspection time causing the SMEs have not been widely applying the consistent quality control system. This study aims to identify the quality control point of ground coffee, classify the quality following Indonesian National Standard (SNI) requirements and determine the corrective or preventive actions needed to produce a consistent, integrated quality control system for ground coffee SME. System requirements are analyzed based on their complexity in real-world using BPMN 2.0. The system made by clustering method with the K-means algorithm. K-Means divides the data into clusters, with data in one cluster sharing similar features but differing from other clusters. The data given is quality data from the bean and ground coffee quality testing. The final results of this study are three clusters of ground coffee quality and association rules determined by the Association Rule Mining method. These rules may use as a reference in taking corrective actions and preventive actions for a nonconformance product.

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“…In Tables 6, the results obtained from our method are compared with results from ELECTRE [10] and k-means [9]. Also giving the corresponding histograms for the table to illustrate the results.…”

Section: Experiments Studymentioning

confidence: 99%

“…Chen et al [7] and Toloo et al [8] propose an approach to estimate and rank the efficiency of association rules with multiple criteria using a non-parametric approach Data Envelopment Analysis (DEA) the problem here is the difficulty of the changed the fractional linear measure into a linear programming model and the complexity of the resolution. Our previous work [9] is to partition the association rules into K disjoint clusters and then classify the obtained clusters from the best to the worst by using an approach based on k-means algorithm the inconvenient of this work is it can't define the number of association rules according to the user's needs, and it is not fixed and related to the number of clusters choosing. Another our previous work [10] use ELECTRE1 to select the most interesting using a set of criteria.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Select the Interesting Association Rules with Multiple Criteria

Dahbi¹,

Jabri²,

Balouki³

et al. 2017

IJIES

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Abstract:Using the association rules in datamining is one of the most relevant techniques in modern society, aiming to extract the interesting correlation and relation among sets of items or products in large transactional databases. The huge number of extracted association rules represents the main problem that a decision maker can face. Hence, the knowledge post-processing phase becomes very important and challenging to define the most interesting association rules, many interestingness measures have been proposed. Currently, there is no optimal measure that can be selected to evaluate the extracted association rules. To bypass this problem, we propose an approach based on multi-criteria optimization aiming to find a good compromise without excluding any measures. The experiments performed on numerous benchmark datasets show that the proposed algorithm is properly reducing a large number of association rules and keeping the most significant and interesting ones compared to other approaches which illustrate the efficiency and the applicability our approach.

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Classification of association rules based on K-means algorithm

Cited by 11 publications

References 11 publications

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Analysis and design of quality control system in ground coffee SMEs

A New Method to Select the Interesting Association Rules with Multiple Criteria

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