Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems

Keogh, Eamonn; Li, Wei; Xi, Xiaopeng; Lonardi, Stefano; Shieh, Jin; Sirowy, Scott

doi:10.1109/icdm.2006.90

Cited by 40 publications

(25 citation statements)

References 9 publications

(8 reference statements)

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“…Post et al [29] proposed 3D iconic techniques for feature visualization. Keogh et al [24] proposed to use colored bitmap to encode time series data.…”

Section: Icon-based Multivariate Visualizationsmentioning

confidence: 99%

DICON: Interactive Visual Analysis of Multidimensional Clusters

Cao

Gotz

Sun

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. DICON is a dynamic icon-based visualization technique that helps users understand, evaluate, and adjust complex multidimensional clusters. It provides visual cues describing the quality of a cluster as well as its multiple attributes, and can be embedded within many kinds of visualizations such as maps, scatter plots, and graphs.Abstract-Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.

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“…Post et al [29] proposed 3D iconic techniques for feature visualization. Keogh et al [24] proposed to use colored bitmap to encode time series data.…”

Section: Icon-based Multivariate Visualizationsmentioning

confidence: 99%

DICON: Interactive Visual Analysis of Multidimensional Clusters

Cao

Gotz

Sun

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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“…The raw signals have information extracted from them regarding the frequencies of short " sub-patterns" . These raw counts of sub-patterns are recorded in a square matrix, and the Euclidean distance between these matrices can effectively capture the similarity of the signals, which can then be used as an input into classification, clustering and anomaly detection algorithms [13] [14]. While it is not necessary for classification algorithms, we can optionally map the values in the matrices to a colormap to allow human subjective interpretation of similarity, and higher level interpretation of the data.…”

Section: Monitoring Insects In Real-timementioning

confidence: 99%

“…We further compact the data by aggregating the SAX representations of segments of data to create a square matrix of fixed length or Time Series Bitmaps (TSBs) [14] [13].…”

Section: Our Algorithm In Contextmentioning

confidence: 99%

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Real-Time Classification of Streaming Sensor Data

Kasetty

Stafford

Walker

et al. 2008

2008 20th IEEE International Conference on Tools With Artificial Intelligence

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“…The common cliché that "a picture is worth a thousand words" advocates the use of visual representation and visualization techniques [7,8,18]. Showing a collection of frequent itemsets in graphical form can help users understand the nature of the information and show the relations embedded in the data.…”

Section: Introductionmentioning

confidence: 99%