Clustering by Using the Way of Atomic Fission

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our “High-Dimensional Sharpened DR” (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.

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“…59,62,63 The data set has N = 2000 samples with n = 7 dimensions and is known to have four well-separated clusters. 64…”

Section: Resultsmentioning

confidence: 99%

Visual cluster separation using high-dimensional sharpened dimensionality reduction

Kim¹,

Telea

Trager

et al. 2022

Information Visualization

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“…59,62,63 The data set has N = 2000 samples with n = 7 dimensions and is known to have four wellseparated clusters. 64 Olive oil: The data has N = 572 samples of olive oil with n = 8 dimensions (fatty acid concentrations), with ground-truth label denoting one of the locations in Italy from where the oil was collected. The location consists of three super-classes (North, South, and the island of Sardinia) and sub-classes (three from the North, four from the South, and two from Sardinia).…”

Section: Real-world Data: Qualitative Evaluationmentioning

confidence: 99%

MING: An interpretative support method for visual exploration of multidimensional data

Colange

Vuillon

Lespinats

et al. 2022

Information Visualization

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Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.

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A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

Han¹,

Lu²,

Xu³

2021

Preprint

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Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. In this article, we propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method. The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.

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Clustering by Using the Way of Atomic Fission

Cited by 3 publications

References 72 publications

Visual cluster separation using high-dimensional sharpened dimensionality reduction

Visual cluster separation using high-dimensional sharpened dimensionality reduction

MING: An interpretative support method for visual exploration of multidimensional data

A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

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