A New Fuzzy Clustering Algorithm on Association Rules for Knowledge Management

Pi, Dechang; Qin, Xiaolin

doi:10.3923/itj.2008.119.124

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…The datasets used for the proposed predicate rule mining algorithm are zoo, breast cancer and car evaluation that is commonly cited in the literature for ARM (Alatas, 2012;Dechang & Xiaolin, 2008). The dataset is available at the UCI Machine Learning Repository (Bache & Lichman, 2013).…”

Section: Dataset Descriptionmentioning

confidence: 99%

Mining predicate rules without minimum support threshold

Ahmad

Sim

Shah

et al. 2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

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Section: Dataset Descriptionmentioning

confidence: 99%

Mining predicate rules without minimum support threshold

Ahmad

Sim

Shah

et al. 2021

KJS publishes peer-review articles in Mathematics, Computer Sci

View full text Add to dashboard Cite

show abstract

“…Reducing redundant resulting rules was proposed as a good method to decrease the number of rules, which is done in two steps, first the rules that have the similar meaning are identified and then eliminated them [33]. Cover methods processed using clustering and selecting participants within clusters have been studied by many researchers, such as cover structure [35] For cover structure method, the [36] formed cluster of rules based on the structure distance of antecedent. A cover structure is to select the most representative rule from each cluster.…”

Section: Reducing the Rulesmentioning

confidence: 99%

3D Matrix-Based Visualization System of Association Rules

Zhang

Chang

Ristaniemi

et al. 2017

2017 IEEE International Conference on Computer and Information Technology (CIT)

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With the growing number of association rules, it becomes more and more difficult for users to explore interesting rules due to its nature complexity. Studies base on human perception and intuition show that graphical representation could be a better illustration of how to handle data by using the capabilities of the human visual system to seek information. The 3D matrixbased approach visualization system of association rules called 3DMVS was implemented in present study. The main visual representation employed the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed to generate new rules to reduce the dimension of the association rules, which will help users to find more important items in the new rule. Additionally, several interactions such as sorting, filtering, zoom and rotation, facilitate decision makers to explore the rules they are interested in various aspects. Finally, various evaluation techniques have been employed to assess the system from a logical reasoning point of view.

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“…Rapid association rule mining [50],Generalized association rule mining [51,52,53] ,Fuzzy association rule mining [54] Mining, Distributed association rule mining [55], Association rule mining using multi criteria decision methods (Global profit weight method) [56], Frequent item sets using vertical layout [57], Maximal and closed frequent pattern mining algorithms [57], Multi dimensional and Quantitative association rule mining algorithms [57], Sequential association rule mining algorithms [57], Incremental association rule mining algorithms [57], Image association rule mining [3] and Association rule mining for clustering [58,59,60] are seen in the literature. The comparison scheme provides a frame work which clearly shows the search type, number of scans required(k-represent number of items) and data structure of the various association rule mining algorithms.…”

Section: Other Important Association Rule Mining Algorithmsmentioning

confidence: 99%

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

Suba¹,

T²

2012

IJCA

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Data mining helps in doing automated extraction and generating predictive information from large amount of data. The association rule mining is one of the important area of research in Data mining. The Association rule mining identifies the useful associations or relationship among big set of data items. In this paper, we provide the important concepts of Association rule mining and existing algorithms and their effectiveness and drawbacks. The references provided in this paper covered the main theoretical issues and guiding the researcher in an interesting research directions that have yet to be discovered.

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A New Fuzzy Clustering Algorithm on Association Rules for Knowledge Management

Cited by 10 publications

References 14 publications

Mining predicate rules without minimum support threshold

Mining predicate rules without minimum support threshold

3D Matrix-Based Visualization System of Association Rules

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

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