Semi-supervised visual clustering for spherical coordinates systems

Chidlovskii, Boris; Lecerf, Loïc

doi:10.1145/1363686.1363892

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The cluster detection of Star Coordinates not only improves the efficiency of axis operations with higher cluster quality, but also allows users to analyze the relationship between cluster and data attributes. To achieve this goal, Approximated Silhouette Index (ASI) could be used [17] to assess cluster quality based on inter-cluster distance and intra-cluster distance. This approach requires the construction of an SI view to inform the user of the quality of the real-time projection.…”

Section: Cluster Detectionmentioning

confidence: 99%

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

He¹,

Chen²,

Li³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions, and evaluate our approach through case studies of an open-pit mine truck dataset. The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

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Section: Cluster Detectionmentioning

confidence: 99%

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

He¹,

Chen²,

Li³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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i-tStar (3D): Three-Dimensional Interactive Trajectory Star Coordinates

Chen

Wang

et al. 2020

2020 International Conference on Information and Communication Technology Convergence (ICTC)

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DClusterE

Zhang

2012

ACM Trans. Intell. Syst. Technol.

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Over the last decade, document clustering, as one of the key tasks in information organization and navigation, has been widely studied. Many algorithms have been developed for addressing various challenges in document clustering and for improving clustering performance. However, relatively few research efforts have been reported on evaluating and understanding document clustering results. In this article, we present DClusterE , a comprehensive and effective framework for document clustering evaluation and understanding using information visualization. DClusterE integrates cluster validation with user interactions and offers rich visualization tools for users to examine document clustering results from multiple perspectives. In particular, through informative views including force-directed layout view, matrix view, and cluster view, DClusterE provides not only different aspects of document inter/intra-clustering structures, but also the corresponding relationship between clustering results and the ground truth. Additionally, DClusterE supports general user interactions such as zoom in/out, browsing, and interactive access of the documents at different levels. Two new techniques are proposed to implement DClusterE : (1) A novel multiplicative update algorithm (MUA) for matrix reordering to generate narrow-banded (or clustered) nonzero patterns from documents. Combined with coarse seriation, MUA is able to provide better visualization of the cluster structures. (2) A Mallows-distance-based algorithm for establishing the relationship between the clustering results and the ground truth, which serves as the basis for coloring schemes. Experiments and user studies are conducted to demonstrate the effectiveness and efficiency of DClusterE .

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Semi-supervised visual clustering for spherical coordinates systems

Cited by 4 publications

References 10 publications

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

i-tStar (3D): Three-Dimensional Interactive Trajectory Star Coordinates

DClusterE

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