Swarm intelligence for self-organized clustering

Thrun, Michael C.; Ultsch, Alfred

doi:10.1016/j.artint.2020.103237

Cited by 70 publications

(62 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, ordering features by distribution shapes proved to be helpful if the performance of classifiers is evaluated by cross-validations [59]. If the advantages are combined with the ggplot2 syntax, they provide detailed error probability comparisons [60] with a high data to ink ratio (c.f. [55] (p. 96).…”

Section: Plos Onementioning

confidence: 99%

Analyzing the fine structure of distributions

2020

Self Cite

View full text Add to dashboard Cite

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to nonexperts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.

show abstract

Section: Plos Onementioning

confidence: 99%

Analyzing the fine structure of distributions

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, a non-linear dimensionality reduction (DR) approach was used, which exploits the concepts of swarm intelligence, self-organization, and emergence. The swarm projects high-dimensional data onto a two-dimensional plane by using intelligent agents operating on a toroidal and polar grid [38]. Hence, it is called polar swarm (Pswarm).…”

Section: B Identification Of Data Cluster Structures: Databionic Swarmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Therefore, we propose a novel methodology for chemical grouping and provenance analysis of archaeological material using Databionic swarm (DBS) [38]. Its coexistence of projection and clustering allows exploring cluster structures through a topographic map without any implicit assumptions about the data [38]. To assess the effectiveness of the method, we analyzed with a portable x-ray fluorescence (pXRF) spectrometer a set of 256 obsidian artifacts recovered from recent archaeological excavations at the site of Xalasco [39][40][41], located northeast of Tlaxcala state in Central Mexico.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials

2020

Self Cite

View full text Add to dashboard Cite

In provenance analysis, identifying the origin of the archaeological artifacts plays a significant role. Usually, this problem is addressed by discovering natural groups in data measured with spectroscopic techniques. Then, principal component and classical partitioning cluster analysis are employed to reveal the groups that supposedly define the origin of the investigated artefacts. However, this work shows that maximizing the variance and searching for specific cluster structures can be misleading because it fails to discriminate clearly the different archeological sources. In contrast, the new methodology reveals several acknowledged geological sources present in the materials through the exploitation of emergence and swarm intelligence without prior assumptions about the data structures. A combination of unsupervised and semi-supervised machine learning and chemometric is applied on samples of Mesoamerican geological sources and obsidian artefacts collected from the archaeological site of Xalasco in Mexico. The analysis of the artifacts showed a preference of Xalasco inhabitants to local obsidian deposits. The results show that this approach, in terms of robustness, is suitable for handling unbiased quantitative spectral analysis of archaeological materials revealing the natural groups of archeological data.

show abstract

“…The last part shows the application in three examples. The method is part of [30] ; it is a co-submission of that article (ARTINT_103237) and the method's description originates from several sections of the Ph.D. thesis, “Projection-Based Clustering through Self-Organization and Swarm Intelligence” [27] .…”

mentioning

confidence: 99%

Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods

Thrun

Ultsch

2020

MethodsX

Self Cite

View full text Add to dashboard Cite

Projections are conventional methods of dimensionality reduction for information visualization used to transform high-dimensional data into low dimensional space. If the projection method restricts the output space to two dimensions, the result is a scatter plot. The goal of this scatter plot is to visualize the relative relationships between high-dimensional data points that build up distance and density-based structures. However, the Johnson–Lindenstrauss lemma states that the two-dimensional similarities in the scatter plot cannot coercively represent high-dimensional structures. Here, a simplified emergent self-organizing map uses the projected points of such a scatter plot in combination with the dataset in order to compute the generalized U-matrix. The generalized U-matrix defines the visualization of a topographic map depicting the misrepresentations of projected points with regards to a given dimensionality reduction method and the dataset. The topographic map provides accurate information about the high-dimensional distance and density based structures of high-dimensional data if an appropriate dimensionality reduction method is selected. The topographic map can uncover the absence of distance-based structures. The topographic map reveals the number of clusters in a dataset as the number of valleys.

show abstract

Swarm intelligence for self-organized clustering

Cited by 70 publications

References 63 publications

Analyzing the fine structure of distributions

Analyzing the fine structure of distributions

Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials

Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods

Contact Info

Product

Resources

About