Tests for Hierarchical Structure in Random Data Sets

1968 ◽  
Vol 17 (4) ◽  
pp. 407 ◽  
Author(s):  
F. James Rohlf ◽  
David R. Fisher
Keyword(s):  
Hierarchical Structure ◽  
Data Sets ◽  
Random Data

Tests for Hierarchical Structure in Random Data Sets

1968 ◽  
Vol 17 (4) ◽  
pp. 407-412 ◽  
Author(s):  
F. J. Rohlf ◽  
D. R. Fisher
Keyword(s):  
Hierarchical Structure ◽  
Data Sets ◽  
Random Data

Beamtrees: Compact Visualization of Large Hierarchies

2003 ◽  
Vol 2 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Frank van Ham ◽  
Jarke J. van Wijk
Keyword(s):  
Hierarchical Structure ◽  
User Study ◽  
Three Dimensional ◽  
New Method ◽  
Data Sets ◽  
Two Dimensional ◽  
Hierarchical Data ◽  
Organization Structures ◽  
The Hierarchical Structure

Beamtrees are a new method for the visualization of large hierarchical data sets, such as directory structures and organization structures. Nodes are shown as stacked circular beams such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. Both a two-dimensional and a three-dimensional variant are presented. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

An Initial Point Selection Algorithm for K-Means Clustering

2013 ◽  
Vol 791-793 ◽  
pp. 1289-1292
Author(s):  
Le Qiang Bai ◽  
Yan Yao Zhou ◽  
Shi Hong Zhang
Keyword(s):  
Initial Point ◽  
Data Sets ◽  
Similar Data ◽  
Selection Algorithm ◽  
Point Selection ◽  
Random Data ◽  
Data Object ◽  
Clustering Center ◽  
Data Objects ◽  
Standard Sets

Aiming at the problem of K-Means algorithm which is sensitive to select initial clustering center, this paper proposes a kind of initial point of K-Means algorithm. The algorithm processes the properties of the data objects, which determines the density of data object by counting the number of similar data objects and selects the center of categories according to the density of data object. The cluster numbers given and the UCI standard sets of data and the random data sets used, the clustering results demonstrate that the proposed algorithm has good stability, accuracy.

Fox and Brown's 'Random Data Sets' Are Not Random

Oikos ◽  
1995 ◽  
Vol 74 (3) ◽  
pp. 543 ◽  
Author(s):  
J. Bastow Wilson
Keyword(s):  
Data Sets ◽  
Random Data

scholarly journals SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data

2013 ◽  
Vol 42 (5) ◽  
pp. e35-e35 ◽  
Author(s):  
Jun Ding ◽  
Haiyan Hu ◽  
Xiaoman Li
Keyword(s):  
Motif Discovery ◽  
De Novo ◽  
Data Sets ◽  
Random Data ◽  
Data Set ◽  
Binding Motifs ◽  
Gene Transcriptional Regulation ◽  
Novel Approach ◽  
De Novo Motif Discovery ◽  
Systematic Identification

Abstract The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annotated motifs even though the number of known motifs is limited. To simplify the problem, de novo motif discovery methods often neglect underrepresented motifs in ChIP-seq peak regions. To address these issues, we developed a novel approach called SIOMICS to de novo discover motifs from ChIP-seq data. Tested on 13 ChIP-seq data sets, SIOMICS identified motifs of many known and new cofactors. Tested on 13 simulated random data sets, SIOMICS discovered no motif in any data set. Compared with two recently developed methods for motif discovery, SIOMICS shows advantages in terms of speed, the number of known cofactor motifs predicted in experimental data sets and the number of false motifs predicted in random data sets. The SIOMICS software is freely available at http://eecs.ucf.edu/∼xiaoman/SIOMICS/SIOMICS.html.

HIERARCHICAL SPHERICAL CLUSTERING

2002 ◽  
Vol 10 (02) ◽  
pp. 157-172 ◽  
Author(s):  
VICENÇ TORRA ◽  
SADAAKI MIYAMOTO
Keyword(s):  
Hierarchical Structure ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Alternative Representation ◽  
Initial Assignment ◽  
Concentric Spheres ◽  
Sammon’S Mapping ◽  
Hierarchical Clustering Algorithm ◽  
Structure Lead ◽  
3D Representations

This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data. The algorithm builds a hierarchical structure (a dendrogram) in such a way that different cuts of the structure lead to different partitions of the surface of the sphere. This can be seen as a set of concentric spheres, each one being of different granularity. Also, to obtain an initial assignment of the data on the surface of the sphere, a method based on Sammon's mapping has been developed.

scholarly journals HIERARCHICAL STRUCTURE OF DENTAL DATA IN THE RANDOM EFFECTS INCLUSION APPROACH

2018 ◽  
Vol 36 (3) ◽  
pp. 700
Author(s):  
Tiago Peres da Silva SUGUIURA ◽  
Omar Cléo Neves PEREIRA ◽  
Waenya Fernandez de CARVALHO ◽  
Isolde Terezinha Santos PREVIDELLI
Keyword(s):  
Hierarchical Structure ◽  
Linear Models ◽  
Intraclass Correlation ◽  
Hierarchical Structures ◽  
Aluminium Chloride ◽  
Data Sets ◽  
Complex Structures ◽  
The Hierarchical Structure ◽  
Level Model ◽  
Multilevel Linear Models

Data sets with complex structures is increasingly common in dental research. As consequences, statistical  methods to analyze and interpret these data must be efficient and robust. Hierarchical structures is one of  the most common kind of complex structures, and a proper approach is required. The multilevel modeling used to study hierarchical structures is a powerful tool which allows the collected data to be  analyzes in several levels. This study has as objective to make a literature review on multilevel linear models and to illustrate a three level model through a matrix procedure, without the use of specific software to estimate the parameters. With this model, we analyzed the vertical gingival retraction when using the substances: Naphazoline Chloridrate, Aluminium Chloride and without any substance. The intraclass correlation coefficient on dental level within patients showed that the hierarchical structure was important to accommodate the dependence within clusters.

scholarly journals Sizes of Permanent Campsite Communities Reflect Constraints on Natural Human Communities

2017 ◽  
Author(s):  
Tobias Kordsmeyer ◽  
Pádraig Mac Carron ◽  
R. I. M. Dunbar
Keyword(s):  
Hierarchical Structure ◽  
Small Scale ◽  
Data Sets ◽  
Effective Community ◽  
Sweet Spots

Both small-scale human societies and personal social networkshave a characteristic hierarchical structure with successivelyinclusive layers of 15, 50, 150, 500, and 1,500 individuals. It hasbeen suggested that these values represent a set of naturalsocial attractors, or “sweet spots,” in organizational terms. Weexploited the new phenomenon of permanent (i.e., residential)campsites to ask whether these values are present in the sizedistribution of the numbers of residents in these naturallysmall-scale communities. In two separate data sets of differentgrain, we find consistent evidence for sites with 50, 150, 500,and maybe 1,500 residents. We infer that these reflect numerical sizes at which communities may in some way be socially optimal. Our data do not allow us to say why this pattern emerges, but the consistency of the results and the fact that thepredetermined sizes of permanent campsites adhere to thispattern suggest that it may arise from the limits on the numberof relationships that make an effective community.

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