Discovering hierarchical structure in normal relational data

2003 ◽

Vol 2 (1) ◽

pp. 3-15 ◽

Cited By ~ 38

Author(s):

Quang Vinh Nguyen ◽

Mao Lin Huang

Keyword(s):

Hierarchical Structure ◽

Dimensional Space ◽

Tree Structure ◽

Relational Data ◽

Two Dimensional ◽

Approach Space ◽

Link Diagram ◽

New Approach ◽

Semantic Zooming ◽

Tree Visualization

This paper describes a new approach, space-optimized tree, for the visualization and navigation of tree-structured relational data. This technique can be used especially for the display of very large hierarchies in a two-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limited screen resolution. Space-optimized tree is a connection+ enclosure visualization approach that recursively positions children of a subtree into polygon areas and still uses a node–link diagram to present the entire hierarchical structure. To be able to handle the navigation of large hierarchies, we use a new hybrid viewing technique that combines two viewing methods, the modified semantic zooming and a focus+ context technique. While the semantic zooming technique can enlarge a particular viewing area by filtering out the rest of tree structure from the visualization, the focus+context technique allows the user to interactively focus, view and browse the entire visual structure with a reasonable high-density display.

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Fuzzified Crisp Relative Cure Hierarchical Clustering for Temporal Relational Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4319.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1234-1241

Keyword(s):

Data Analysis ◽

Hierarchical Structure ◽

Data Clustering ◽

Minimum Time ◽

Space Complexity ◽

Relational Data ◽

Representative Point ◽

Temporal Data ◽

Data Points ◽

Representative Points

A bitemporal data clustering is a significant solution to the diverse problems for finding the intrinsic structure and compact information over temporal data. The temporal data are collected in the series of particular time periods. The various data mining methods have been developed in the temporal relational data analysis. But the accurate analysis was not performed with minimum time. An efficient technique called Fuzzy Crisp Relative Spherical CURE Hierarchy Clustering (FCRSCHC) is introduced for improving the temporal relational data analysis by partitioning the total dataset into different clusters with minimum time as well as space complexity. The CURE hierarchical structure takes the number of scattered temporal data points in the spherical surface for the clustering. After that, ‘k’ number of clusters and the representative points (i.e. cluster centroid) are initialized. Then the distance between the representative point and the temporal data point are calculated using spherical coordinates. The minimum distance between the data points are grouped into a particular cluster. Then the fuzzy memberships between the two cluster representative points are calculated based on the distance metric. The CURE hierarchical structure merges the two clusters based on the crisp relation between the representative points. Then, the newly obtained clusters are validated using the silhouette coefficient to identify the data points are close to its own cluster or their neighboring clusters. Finally, the optimal numbers of clusters are obtained and minimize the incorrect data clustering which improves the accuracy. The experimental evaluation is performed using a bitmeporal dataset with various parameters such as clustering accuracy, false alarm rate, clustering time and space complexity. The results show that FCRSCHC technique improves the clustering accuracy and minimize the time as well as space complexity as compared to the state-of-the-art- works.

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Relationships between hierarchical structure and properties in macromolecular systems

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100126950 ◽

1987 ◽

Vol 45 ◽

pp. 440-443

Author(s):

E. Baer

Keyword(s):

Uniaxial Tension ◽

Hierarchical Structure ◽

Inorganic Material ◽

Hierarchical Structures ◽

Bone Collagen ◽

Structure And Properties ◽

Connective Tissues ◽

Scale Level ◽

Fibrous Protein ◽

Macromolecular Systems

The most advanced macromolecular materials are found in plants and animals, and certainly the connective tissues in mammals are amongst the most advanced macromolecular composites known to mankind. The efficient use of collagen, a fibrous protein, in the design of both soft and hard connective tissues is worthy of comment. Very crudely, in bone collagen serves as a highly efficient binder for the inorganic hydroxyappatite which stiffens the structure. The interactions between the organic fiber of collagen and the inorganic material seem to occur at the nano (scale) level of organization. Epitatic crystallization of the inorganic phase on the fibers has been reported to give a highly anisotropic, stress responsive, structure. Soft connective tissues also have sophisticated oriented hierarchical structures. The collagen fibers are “glued” together by a highly hydrated gel-like proteoglycan matrix. One of the simplest structures of this type is tendon which functions primarily in uniaxial tension as a reinforced elastomeric cable between muscle and bone.

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