WiFIsViz: Effective Visualization of Frequent Itemsets

Leung, Carson K.; Irani, Pourang; Carmichael, Christopher L.

doi:10.1109/icdm.2008.93

Cited by 47 publications

(15 citation statements)

References 14 publications

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“…First, we compared functionality and performance of ContrastViz with some existing systems (e.g., PowerSetViewer [17], FIsViz [11], WiFIsViz [12]). Then, we provide results on our user evaluation on the effectiveness of visualization provided by ContrastViz.…”

Section: Discussionmentioning

confidence: 99%

“…An event is a node in the chain and the connecting edges depict the event-set. PowerSetViewer [17], FIsViz [11], and WiFIsViz [12] all showing only frequent patterns of a single data instance but not the difference (e.g., frequency changes) between two data instances.…”

Section: Background and Related Workmentioning

confidence: 99%

“…So far, a few visualization tools have been developed to support frequent pattern mining. Examples include PowerSetViewer [17], FIsViz [11], FpVAT [9], SocialVis [10], as well as WiFIsViz [12] proposed in ICDM 2008. In general, these visualizers show only the frequent patterns mined from a single database instance.…”

Section: Introductionmentioning

confidence: 99%

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Visually Contrast Two Collections of Frequent Patterns

Carmichael

Hayduk

Leung

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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Frequent pattern mining searches for frequently occurring sets of items or events. While users are interested in finding these frequent patterns in most situations, they may want to compare and contrast the mined frequent patterns in some other situations. For example, store managers may want to find out how the collections of frequently purchased items changed from one season to another. Similarly, regional managers may want to compare the frequently purchased items between two different branches. These are some examples of looking for temporal and/or spatial changes between mined frequent patterns. A visual representation of these patterns would be more comprehensive to users than the long textual list returned by many existing frequent pattern mining algorithms. However, many existing visualizers were not designed to show frequent patterns, let alone show the differences between them. In this paper, we propose a visualization system called ContrastViz that enables users to visualize the mined frequent patterns and their differences.

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Section: Discussionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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Visually Contrast Two Collections of Frequent Patterns

Carmichael

Hayduk

Leung

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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“…Recently, [16] deals with the visualization of frequent itemsets. The authors developed a system, WiFIsViz, for visualizing frequent itemsets based on orthogonal graphs (wiring-type diagrams).…”

Section: Visualization In Data Mining: Related Workmentioning

confidence: 99%

A clustering-based visualization of colocation patterns

Desmier

Flouvat

Selmaoui-Folcher

2011

Proceedings of the 15th Symposium on International Database Engineering &Amp; Applications - IDEAS '11

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Extraction of interesting colocations in geo-referenced data is one of the major tasks in spatial pattern mining. The goal is to find sets of spatial object-types with instances located in the same neighborhood. In this context, the main drawback is the visualization and interpretation of extracted patterns by domain experts. Indeed, common textual representation of colocations loses important spatial information such as the position, the orientation or the spatial distribution of the patterns. To overcome this problem, we propose a new clustering-based visualization technique deeply integrated in the colocation mining algorithm. This new simple, concise and intuitive cartographic visualization considers both spatial information and expert practices. This proposition has been integrated in a Geographic Information System and experimented on a real-world geological data set. Domain experts confirm the added-value of this visualization approach.

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“…Users definitely know whether an item is present in, or absent from, a transaction in databases of precise data. Common pattern mining techniques applied to precise data include the mining of contrast patterns [6], directed acyclic graphs [8], high utility patterns [17], [20], and sequential patterns [18], as well as the stream mining [12] and visual analytics [11] of frequent patterns.…”

Section: Introductionmentioning

confidence: 99%

DISC: Efficient Uncertain Frequent Pattern Mining with Tightened Upper Bounds

MacKinnon

Strauss

Leung

2014

2014 IEEE International Conference on Data Mining Workshop

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UF-growth is a tree-based exact algorithm for mining frequent patterns from uncertain data. While it directly calculates the expected support of a pattern, it requires a significant amount of storage space to capture all existential probability values among the items. To eliminate the extra space requirement of UF-growth, the CUF-growth algorithm combines nodes with the same item by storing an upper bound on expected support. In this paper, we (i) introduce a new concept of domain item-specific capping (DISC) and (ii) propose three new scalable data analytics algorithms that use this concept to achieve a tighter upper bound than CUF-growth. Experimental results show the effectiveness of uncertain frequent pattern mining with tightened upper bounds provided by using the concept of DISC.

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WiFIsViz: Effective Visualization of Frequent Itemsets

Abstract: Abstract

Cited by 47 publications

References 14 publications

Visually Contrast Two Collections of Frequent Patterns

Visually Contrast Two Collections of Frequent Patterns

A clustering-based visualization of colocation patterns

DISC: Efficient Uncertain Frequent Pattern Mining with Tightened Upper Bounds

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