A finite ranked poset and its application in visualization of association rules

Jiang, Baoqing; Han, Chong; Hu, Xiaohua

doi:10.1109/grc.2008.4664764

Cited by 5 publications

(6 citation statements)

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“…There are three classes originally included in the dataset, i.e. 'rings_(-inf-8]', 'rings_ (8)(9)(10)(11)(12)(13)(14)(15)', and 'rings_ (15)(16)(17)(18)(19)(20)(21)(22)'. Thus, the first one is included into 69.95 % of rules as a consequent, the second one in 30.05 % of rules as a consequent, while the third one is not included in any association rule as a consequent and is therefore eliminated from observation.…”

Section: Arm Datasets Creationmentioning

confidence: 99%

“…4. This metro map consists of two metro lines classifying the age of abalone ear shells according to the number of wings into 'rings_(-inf-8]' and 'rings_ (8)(9)(10)(11)(12)(13)(14)(15)' classes. The former has less or equal than 8 rings and starts with a length less than or equal to 0.26 mm, while the latter is higher than 8, but lower than 16 rings and starts with a viscera weight between 0.38025 and 0.570125 grams.…”

Section: Abalone Datasetmentioning

confidence: 99%

“…A very interesting interactive visualization technique, which lets the user navigate through a hierarchy of association rule groups is presented in paper [14]. Authors in paper [15] explores the Hasse diagrams for the visualization of Boolean association rules. Fister et al [16] have proposed a method for identifying dependencies among mined association rules based on a population-based metaheuristics and complex networks.…”

Section: Introductionmentioning

confidence: 99%

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Information Cartography in Association Rule Mining

Fister¹

2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Association Rule Mining is a data mining method for discovering the interesting relations between attributes in a huge transaction database. Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suitable for the user. Recently, an information cartography has been proposed for creating structured summaries of information and visualizing with methodology called "metro maps". This was applied to many problem domains. In the hope of widening its applicability domain, the aim of this study is to develop a method for the automatic creation of metro maps of information obtained by association rule mining. Although the proposed method consists of multiple steps, its core presents metro map construction that is defined in the study as an optimization problem, which is solved using an evolutionary algorithm. Finally, this was applied to four well-known UCI Machine Learning datasets and one sport dataset. Visualizing the resulted metro maps not only justifies the fact this is a suitable tool for presenting structured knowledge hidden in data, but also that they can even tell stories to users.

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Section: Arm Datasets Creationmentioning

confidence: 99%

Section: Abalone Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information Cartography in Association Rule Mining

Fister¹

2022

IEEE Trans. Emerg. Top. Comput. Intell.

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“…A more advanced technique is the Virtual Reality approach, which is introduced for the problem of decision rules generated by inductive methods, rough set algorithms, and others [20]. More propositions for rules visualization are presented in other works [21][22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

The new module for rules discovering and visualization for NovoSpark® Visualizer software

Pilipczuk

Cariowa²

2015

ELECTROTECHNICAL REVIEW

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In this paper we present the new rough sets module for NovoSpark® Visualizer (NV) software. We describe the NV system architecture and the place of the new module in it. We also present the procedure of rough sets analysis with NV software. In addition an example of rules discovering and visualization is provided to evaluate the proposed module. The results show that useful rules are discovered efficiently from the data set. Streszczenie. W artykule zaprezentowano projekt nowego modułu do automatyzacji teorii zbiorów przybliżonych dla oprogramowania NovoSpark® Visualizer (NV). Opisano architekturę systemu oraz wskazano miejsce nowego modułu. Ponadto zaprezentowano przebieg procedury analizy i wizualizacji zbiorów przybliżonych w systemie. Przedstawiono przykład odkrywania i wizualizacji reguł za pomocą opracowanej procedury. W wyniku przeprowadzenia eksperymentu udało się otrzymać szereg użytecznych reguł decyzyjnych. (Nowy moduł odkrywania i wizualizacji reguł dla systemu NovoSpark® Visualizer).

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“…A very interesting interactive visualization technique, which lets the user navigate through a hierarchy of association rule groups is presented in paper [14]. The authors in paper [18] explored the Hasse diagrams for the visualization of Boolean association rules. Fister et al [7] proposed a method for identifying dependencies among mined association rules based on population-based metaheuristics [8,12,13] and complex networks, while the paper of Fister Jr. et al [19] looked for a visualization method capable of telling stories based on mined association rules.…”

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confidence: 99%

Association Rules over Time

Fister

2021

Springer Tracts in Nature-Inspired Computing

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after obtaining the results of specific ML methods, in order to discover any additional knowledge hidden in data, and, thus, help the user to understand the results properly.Association Rule Mining (ARM) is a well-known ML method that normally generates a huge amount of association rules, from which it is hard to make the proper decisions easily. An additional problem is presented by the complex form of the results, which are represented as an implication X ⇒ Y , meaning "'if antecedent X is true then its consequent Y is also true". As a result, there we are confronted with two problems: (1) How to discover the relevant association rules in a huge archive, and (2) How to express the relations between attributes in those implication rules visually in a form that is understandable and interpretable by users.Interestingly, the visualization of association rules has rarely been treated in the literature. Indeed, the papers which referred to these topics can be summarized in the following review: The authors in [27] presented a design that is able to handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping. The authors in [16] show that the use of Mosaic plots and their variant, called Double Decker plots, can be applied for visualizing association rules. Ong et al. [23] prototyped the two visualizations, called grid view and tree view, for visualizing the association rules in their application called CrystalClear. Appice and Buono [2] presented a graph-based visualization that supports data miners in the analysis of multi-level spatial association rules, while Herawan et al. [15] proposed an approach for visualizing soft maximal association rules. A very interesting interactive visualization technique, which lets the user navigate through a hierarchy of association rule groups is presented in paper [14]. The authors in paper [18] explored the Hasse diagrams for the visualization of Boolean association rules. Fister et al. [7] proposed a method for identifying dependencies among mined association rules based on population-based metaheuristics [8,12,13] and complex networks, while the paper of Fister Jr. et al. [19] looked for a visualization method capable of telling stories based on mined association rules. However, there are also generic tools, like the CloseViz [4] and the SPMF open-source data mining library Version 2 [9], specialized primarily in pattern mining, offering visual implementation of discovered data mined by ML algorithms that could also be used for visualization of ARM.This chapter proposes monitoring the performance of an athlete involved in sport training sessions during the past seasons (i.e., years). Thus, these seasons are divided into four time periods, where each of the periods captures a quarter of the data. The performances of the athlete are measured using real data obtained by mobile devices worn by the athlete during the training sessions. The performance data (e.g., duration, a...

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A finite ranked poset and its application in visualization of association rules

Cited by 5 publications

References 6 publications

Information Cartography in Association Rule Mining

Information Cartography in Association Rule Mining

The new module for rules discovering and visualization for NovoSpark® Visualizer software

Association Rules over Time

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