KeyGraph: Visualized Structure Among Event Clusters

Ohsawa, Yukio

doi:10.1007/978-3-662-06230-2_18

Cited by 65 publications

(22 citation statements)

References 3 publications

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“…KeyGraph is a popular chance discovery method [4], [5]. KeyGraph is a data visualization algorithm that uses interactive parameters.…”

Section: Yukihiro Takayama and Ryosuke Sagamentioning

confidence: 99%

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Proposal of Chance Index in Co-occurrence Visualized Network

Takayama¹,

Saga²

2015

IJKE

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Abstract-This study describes a chance discovery method for network that use betweeness centrality and similarity. In prior research of chance discovery, in the chance discovery process, it is required that analysts infer chance from visualized network, because it is difficult that to solve problem like to guess the cause from the data such as non-parametric problem. However, this reasoning process has problem that chance discovery is difficult because chance discovery depends on experience or background knowledge of analysts. Therefore, to solve this problem, we pay attention the mathematical element with the network, and propose chance index that is index of network. Chance index have three calculation methods: the sum of the reciprocal, the product of the reciprocal, and the average reciprocal. Using the proposal method on three kinds of data, results show that proposal method is useful method and chance index that use average reciprocal is most useful calculation method.

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“…KeyGraph is a popular chance discovery method [4], [5]. KeyGraph is a data visualization algorithm that uses interactive parameters.…”

Section: Yukihiro Takayama and Ryosuke Sagamentioning

confidence: 99%

“…discovery [4], [5]. KeyGraph simplifies chance inference by devising a graphical representation when visualizing data.…”

Section: Introductionmentioning

confidence: 99%

Proposal of Chance Index in Co-occurrence Visualized Network

Takayama¹,

Saga²

2015

IJKE

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“…Concerning the important role of information visualization, a variety of information visualization systems have been designed for chance discovery process [4][5][6][7], among which one of the most famous systems is KeyGraph [4]. This section briefly describes the graph generated by KeyGraph.…”

Section: Keygraphmentioning

confidence: 99%

“…The mapping method between the graph generated by KeyGraph [4] and the scenario, which a user draws up from the graph, is proposed for supporting chance dis-Y. Iwase (B) Y. Seo · Y. Takama Tokyo Metropolitan University, 6-6, Asahigaoka, Hino City, 191-0065 Tokyo, Japan e-mail: iwase@krectmt3.tmit.ac.jp covery process.…”

Section: Introductionmentioning

confidence: 99%

Scenario to Data Mapping for Chance Discovery Process

2006

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The mapping method between the graph generated by KeyGraph and the scenario drawn up by a user is proposed for supporting chance discovery process. Although KeyGraph is widely known as one of the effective tools that support the process of chance discovery, further improvement seems to be required, concerning the ambiguity involved in user's interpretation of the graph. The mapping found by the proposed algorithm is used for extracting the data referred to in the scenario and for annotating those in the original data file. The annotated data files are expected to be used for further data analysis as well as for supporting group discussion. Experiments are performed in terms of scenario similarity calculation based on the annotated data set. The results are compared with typical document retrieval method based on Vector space model (VSM), which shows the availability of annotated data in chance discovery process.

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“…There are various methods for the two processes. Most of mining methods concentrate on finding typical events in source dataset, but there is the method called KeyGraph (Ohsawa 2003) designed to find not only typical events but rear. It is the name of a mining method itself and also the diagram generated by it.…”

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confidence: 99%

Gradual-increase extraction of target baskets as preprocess for visualizing simplified scenario maps by KeyGraph

Sakakibara

Ohsawa

2006

Soft Comput

Self Cite

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KeyGraph is one of the powerful methods to support mining some knowledge from huge dataset because of its visualization mechanism. It presents the dataset in network diagram with representative events and their relationships. The data analyst reads its relationships, and supposes scenarios from them. This is conceptually very simple process, but it becomes more difficult when the diagram becomes complex. In this paper, to overcome this difficulty, we develop the pre-process to generate simple network KeyGraph and the scenario supposing process which repeats our pre-process, generation of KeyGraph and supposing scenarios.Keywords Data mining · KeyGraph · Scenario · Data preprocessing · Gradual-increase extraction of baskets Background and objectivesData mining is the method to obtain knowledge from huge dataset. Its most important element is how useful, understandable, and new knowledge we can obtain (Fayyad et al. 1995). In case which the dataset uses small number of variables, we can obtain knowledge relatively

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KeyGraph: Visualized Structure Among Event Clusters

Cited by 65 publications

References 3 publications

Proposal of Chance Index in Co-occurrence Visualized Network

Proposal of Chance Index in Co-occurrence Visualized Network

Scenario to Data Mapping for Chance Discovery Process

Gradual-increase extraction of target baskets as preprocess for visualizing simplified scenario maps by KeyGraph

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