Visualizing association rules in hierarchical groups

Hahsler, Michael; Karpienko, Radoslaw

doi:10.1007/s11573-016-0822-8

Cited by 76 publications

(61 citation statements)

References 36 publications

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“…From the results of association rules, we can see that Hnsc, Skcm and Luad have potential association. Here we used a new visualization technique [15] that enhances matrix-based visualization using grouping of rules via clustering to handle a larger number of rules. The arulesViz package (http://lyle.smu.edu/~mhahsler) of R software was used to make this graph.…”

Section: Exploration the Cancers Association Based On The Associationmentioning

confidence: 99%

Exploration of the cancers association based on somatic data in TCGA

Xia¹,

Hua²,

Zheng³

et al. 2015

Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

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Section: Exploration the Cancers Association Based On The Associationmentioning

confidence: 99%

Exploration of the cancers association based on somatic data in TCGA

Xia¹,

Hua²,

Zheng³

et al. 2015

Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

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“…For obtaining realistic results, the improvement with the combination from two or more approaches is necessary. For example, Hahsler and Karpienko (2017) extracted association rules with hierarchical clustering. For revealing accurate students' imagination in the study, we adopt some mining approaches and statistical approaches, and thereby combine these approaches to analyze and predict students' imagination.…”

Section: Discussionmentioning

confidence: 99%

Using Localized Features for Analyzing College Students’ Imagination

Liu

Liao

2019

EURASIA J MATH SCI T

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The analysis of imagination has become popular in recent years because imagination is one of the key components of creativity and innovation. For extracting students' implicit degrees and thought processes of imagination, we use frequent pattern mining and association rule extraction to localize the features and explain the deep meanings of imagination in the study. By our observations, these two methods may sometimes explore meaningless frequent patterns and rules on a global sparse dataset. In order to eliminate such phenomena when mining with these two methods, we use a localized feature approach called forecast with clustering and integration (FCI) to improve the drawbacks of two methods on a sparse dataset. The approach consists of two strategies. One is clustering and the other is the prediction based on integration from (1) frequent patterns, (2) association rule pruning with correlation, and (3) forecast with linear regression. The former strategy can reduce the number of samples and highlight the features of imagination and the latter strategy can prune meaningless information and predict the trend of scores from imagination input data. Experimental results show both proposed approaches can localize special features, thereby providing supervisors with meaningful information about students' degrees and thought processes of imagination. Localized Features for Analyzing Imagination 2 / 16 relative supports, we concentrate on localized feature strategies to evaluate frequent items and, thereby, infer the kernel association rules for enhancement.The first localized feature strategy is top-K clustering. By top-K clustering, the data can be separated into several groups, and the representative features can be explored from the largest K groups individually. Generally, clustering, an unsupervised learning, can aggregate similar data into several groups and use in constructing the data model from a dataset with unknown categories. The clustered groups can be expressed as the localized features of all input data because clustering can separate global data into several local data. To localize students' imagination, in this study, we use the density-based clustering scheme, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), for aggregating input data. Unlike centroid-based clustering approaches such as kmeans approaches, DBSCAN aggregates input data without restricting circle-shaped clusters because it considers the relationships such as Euclidean distance or cosine similarity between two data points. This feature of relationship results in grouping the similar input data into the same clusters. By contrast, the results obtained using a centroid-based clustering approach may be different due to the locations of the initial centroids. Hence, clustering using DBSCAN can yield more stable results than that with any centroid-based clustering approach. After clustering, researchers can choose the largest K clusters for analysis. These K clusters contain the features that most students have. Therefore, ...

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“…Therefore, the rules need to be associated with the locations of the wells. However, the existing visualization methods for association rules fall short: most methods, such as scatter plots, matrix visualizations, graphs, mosaic plots and parallel coordinate plots [7][8][9][10][11][12][13], have been designed to help identify interesting association rules discovered from non-spatial datasets and, thus, focus more on the visualization of the rule content characteristics.…”

Section: Introductionmentioning

confidence: 99%

Geovisualization for Association Rule Mining in Oil and Gas Well Data

Sun

Wang

2017

IJGI

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Association rule mining on oil and gas data has recently been successfully used to help understand reservoirs; therefore, the visualization and understanding of the discovered association rules based on well locations and subsequent predictions based on the applicable areas of the rules are important. In this paper, two visualization methods-point-and surface-based geovisualization-are proposed for association rules from oil and gas well data. The point-based method represents association rules based on well locations, and the surface-based method represents potentially applicable areas through spatial interpolation and visualization. A case study has been carried out on a real cold production oil well dataset in western Alberta, Canada, and, the results illustrate the feasibility of the proposed geovisualization methods.

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Visualizing association rules in hierarchical groups

Cited by 76 publications

References 36 publications

Exploration of the cancers association based on somatic data in TCGA

Exploration of the cancers association based on somatic data in TCGA

Using Localized Features for Analyzing College Students’ Imagination

Geovisualization for Association Rule Mining in Oil and Gas Well Data

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