HiPaR: Hierarchical Pattern-Aided Regression

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.

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Biology, Management, and Protection of North American Sturgeon

Biology, Management, and Protection of North American Sturgeon ◽

10.47886/9781888569360.ch11 ◽

2002 ◽

Keyword(s):

Water Quality ◽

Downstream Effects ◽

Poor Water Quality ◽

Negative Effect ◽

One Step ◽

Second Tier ◽

Adult Habitat ◽

Historical Levels ◽

Hierarchical Pattern ◽

Related Mortality

<em>Abstract.</em>—Five of the nine populations of white sturgeon <em>Acipenser transmontanus</em>, located between dams on the Middle Snake River, have declined from historical levels and are now at risk of extinction. One step towards more effectively protecting and managing these nine populations is ranking factors that influence recruitment in each of these river segments. We developed a model to suggest which of seven mechanistic factors contribute most to lost recruitment in each river segment: (1) temperature-related mortality during incubation, (2) flow-related mortality during incubation, (3) downstream export of larvae, (4) limitation of juvenile and adult habitat, (5) mortality of all ages during summer episodes of poor water quality in reservoirs, (6) entrainment mortality of juveniles and adults, and (7) angling mortality. We simulated recruitment with, and without, each of the seven factors, over a typical series of hydrologic years. We found a hierarchical pattern of limitation. In the first tier, river segments with severe water quality problems grouped together. Poor water quality during summer had a strong negative effect on recruitment in the river segments between Swan Falls Dam and Hell’s Canyon Dam. In the second tier, river segments with better water quality divided into short river segments and longer river segments. Populations in short river segments were limited by larval export. Populations in longer river segments tended to be less strongly limited by any one factor. We also found that downstream effects could be important, suggesting that linked populations cannot be viewed in isolation. In two cases, the effects of a factor on an upstream population had a significant influence on its downstream neighbors.

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Simulated annealing for hierarchical pattern detection and seismic applications

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) ◽

10.1109/ijcnn.2008.4633960 ◽

2008 ◽

Author(s):

Kou-Yuan Huang ◽

Ying-Liang Chou

Keyword(s):

Simulated Annealing ◽

Pattern Detection ◽

Hierarchical Pattern ◽

Seismic Applications

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Hierarchical pattern recognition in milking parameters predicts mastitis prevalence

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2018.02.003 ◽

2018 ◽

Vol 147 ◽

pp. 6-11 ◽

Cited By ~ 16

Author(s):

Esmaeil Ebrahimie ◽

Faezeh Ebrahimi ◽

Mansour Ebrahimi ◽

Sarah Tomlinson ◽

Kiro R. Petrovski

Keyword(s):

Pattern Recognition ◽

Hierarchical Pattern

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SELF-ORGANIZED CORONA GRAPHS: A DETERMINISTIC COMPLEX NETWORK MODEL WITH HIERARCHICAL STRUCTURE

Advances in Complex Systems ◽

10.1142/s021952591950019x ◽

2019 ◽

Vol 22 (06) ◽

pp. 1950019

Author(s):

ROHAN SHARMA ◽

BIBHAS ADHIKARI ◽

TYLL KRUEGER

Keyword(s):

Power Law ◽

Small World ◽

Self Organization ◽

Scale Free ◽

Self Organized ◽

Power Law Exponent ◽

Growing Networks ◽

Growing Network ◽

Hierarchical Pattern ◽

Corona Graphs

In this paper, we propose a self-organization mechanism for newly appeared nodes during the formation of corona graphs that define a hierarchical pattern in the resulting corona graphs and we call it self-organized corona graphs (SoCG). We show that the degree distribution of SoCG follows power-law in its tail with power-law exponent approximately 2. We also show that the diameter is less equal to 4 for SoCG defined by any seed graph and for certain seed graphs, the diameter remains constant during its formation. We derive lower bounds of clustering coefficients of SoCG defined by certain seed graphs. Thus, the proposed SoCG can be considered as a growing network generative model which is defined by using the corona graphs and a self-organization process such that the resulting graphs are scale-free small-world highly clustered growing networks. The SoCG defined by a seed graph can also be considered as a network with a desired motif which is the seed graph itself.

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