Visualization of Cluster Changes by Comparing Self-organizing Maps

Denny,; Squire, David

doi:10.1007/11430919_48

Cited by 17 publications

(18 citation statements)

References 13 publications

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“…In [13], Denny et al proposed a SOM-based method for visualizing cluster changes between two datasets. Two SOMs are constructed from the first and the second datasets.…”

Section: Related Workmentioning

confidence: 99%

Visualizing Cluster Structures and Their Changes over Time by Two-Step Application of Self-Organizing Maps

Ishikawa¹

2012

New Frontiers in Applied Data Mining

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Abstract. In this paper, a novel method for visualizing cluster structures and their changes over time is proposed. Clustering is achieved by two-step application of self-organizing maps (SOMs). By two-step application of SOMs, each cluster is assigned an angle and a color. Similar clusters are assigned similar ones. By using colors and angles, cluster structures are visualized in several fashions. In those visualizations, it is easy to identify similar clusters and to see degrees of cluster separations. Thus, we can visually decide whether some clusters should be grouped or separated. Colors and angles are also used to make clusters in multiple datasets from different time periods comparable. Even if they belong to different periods, similar clusters are assigned similar colors and angles, thus it is easy to recognize that which cluster has grown or which one has diminished in time. As an example, the proposed method is applied to a collection of Japanese news articles. Experimental results show that the proposed method can clearly visualize cluster structures and their changes over time, even when multiple datasets from different time periods are concerned.

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“…In [13], Denny et al proposed a SOM-based method for visualizing cluster changes between two datasets. Two SOMs are constructed from the first and the second datasets.…”

Section: Related Workmentioning

confidence: 99%

Visualizing Cluster Structures and Their Changes over Time by Two-Step Application of Self-Organizing Maps

Ishikawa¹

2012

New Frontiers in Applied Data Mining

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“…Denny and Squire [5] proposed a SOM training method and SOM-based visualization techniques that are capable of explaining the clustering results of a second dataset in terms of the clustering result of a first dataset by using color and position linking. These visualization techniques can relate two clustering results which can show the following structural changes: changes in cluster size, centroid movements, new clusters, cluster splitting, missing clusters, and cluster merging.…”

Section: Related Workmentioning

confidence: 99%

“…We define relative density RD M (τ2)/M (τ1) (v) as the ratio of the area density at location of vector v on map M (τ 2 ) to the area density at the same location on the reference map M (τ 1 ), as shown in Equation 5:…”

Section: Relative Density Definitionmentioning

confidence: 99%

“…In order to be able to compare two maps that are trained using datasets D(τ 1 ) and D(τ 2 ), the orientation of map M (τ 1 ) and M (τ 2 ) must be the same. Therefore, the following map training procedure, as proposed in [5], is used.…”

Section: Relative Densitymentioning

confidence: 99%

“…SOMs have several advantages in temporal cluster analysis. They are able to relate clustering results by linking multiple visualizations, and they can detect various types of cluster changes, including emerging and disappearing clusters [5]. Furthermore, SOMs create a smaller but representative dataset, and they have topology preservation properties [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ReDSOM: Relative Density Visualization of Temporal Changes in Cluster Structures Using Self-Organizing Maps

Denny

Williams

Christen

2008

2008 Eighth IEEE International Conference on Data Mining

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We introduce a Self-Organizing Map (SOM) based visualization method that compares cluster structures in temporal datasets using Relative Density SOM (ReDSOM) visualization. Our method, combined with a distance matrix-based visualization, is capable of visually identifying emerging clusters, disappearing clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. For example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such cluster changes is important in many contexts, including the exploration of changes in population behavior in the context of compliance and fraud in taxation.

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Contrast and change mining

Boettcher

2011

WIREs Data Min & Knowl

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Because the world with its markets, innovations, and customers is changing faster than ever before, the key to survival for businesses is the ability to detect, assess, and respond to changing conditions timely and intelligently. Understanding changes and reacting to or acting upon them therefore become a strategic issue not only for companies but also in many other domains. The corresponding need for knowledge has been answered by data mining research by proposing a multitude of methods for analyzing different aspects of change. This article provides an overview of recent works on methods for change analysis, thereby focusing on contrast mining and change mining, the two emerging subfields of contemporary data mining research.

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Visualization of Cluster Changes by Comparing Self-organizing Maps

Cited by 17 publications

References 13 publications

Visualizing Cluster Structures and Their Changes over Time by Two-Step Application of Self-Organizing Maps

Visualizing Cluster Structures and Their Changes over Time by Two-Step Application of Self-Organizing Maps

ReDSOM: Relative Density Visualization of Temporal Changes in Cluster Structures Using Self-Organizing Maps

Contrast and change mining

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