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After shearing with emulsifier, fractal-like shape clusters are obtained through a diffusion limited aggregation process.
It is designed for detecting inherent group structures with arbitrary shape clusters among multidimensional measurement data without any a priori information.
Compared with previous computer-assisted systems, built on a pre-defined classification of prototype shapes, we require no a priori assumptions of the flow shape clusters to be discovered.
The shape clusters of O. gularis, O. austriaca and O. pseudodolium are slightly overlapping.
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It is here that the next China may be taking shape: clustered around the shopping tills of Chongqing, not the assembly lines of Shenzhen.
The visual exploratory data analysis techniques showed that the proposed methodology minimized the total within-cluster variance and thus, improved the leaf shape clustering.
The intrinsic flow shape clustering is performed with a modification of the Isodata algorithm, and the K-means clustering is used as a reference in comparison studies.
The flow shape clustering aims at reducing redundant information in the data, and thereby decreases the time needed to score overnight sleep recordings.
In order to seamlessly capture the similarity between feature descriptors, we perform shape clustering on mid-level features that are generated via graph regularized sparse coding.
In this paper, we present a graph-theoretic framework for 3D shape clustering using the biharmonic distance map and graph regularized sparse coding.
This production route guarantees a good cohesion of the steel/reinforcement interface and the control of the main parameters that determine the final properties of the product (size, shape, clustering of reinforcements).
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