A Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype data

2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Sui-Pi Chen ◽  
Guan-Hua Huang
Keyword(s):  
High Dimensional ◽  
Bayesian Clustering ◽  
Gene Interactions ◽  
Genotype Data ◽  
Clustering Approach

scholarly journals A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1132
Author(s):  
Deting Kong ◽  
Yuan Wang ◽  
Xinyan Wu ◽  
Xiyu Liu ◽  
Jianhua Qu ◽  
...  
Keyword(s):  
Dimensional Space ◽  
P Systems ◽  
High Dimensional ◽  
P System ◽  
Inhibitory Influence ◽  
Spiking Neural P Systems ◽  
Clustering Approach ◽  
Spatial Cluster Analysis ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

In this paper, we propose a novel clustering approach based on P systems and grid- density strategy. We present grid-density based approach for clustering high dimensional data, which first projects the data patterns on a two-dimensional space to overcome the curse of dimensionality problem. Then, through meshing the plane with grid lines and deleting sparse grids, clusters are found out. In particular, we present weighted spiking neural P systems with anti-spikes and astrocyte (WSNPA2 in short) to implement grid-density based approach in parallel. Each neuron in weighted SN P system contains a spike, which can be expressed by a computable real number. Spikes and anti-spikes are inspired by neurons communicating through excitatory and inhibitory impulses. Astrocytes have excitatory and inhibitory influence on synapses. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

scholarly journals A Clustering Approach for Characterizing Voter Types: An Application to High-Dimensional Ballot and Survey Data

2020 ◽  
Author(s):  
Shiro Kuriwaki
Keyword(s):  
Survey Data ◽  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional ◽  
Palm Beach County ◽  
Spatial Voting ◽  
Clustering Approach ◽  
Swing Voters ◽  
State And Local ◽  
Spatial Voting Model

Large-scale ballot and survey data hold the potential to uncover the prevalence of swing voters and strong partisans in the electorate. However, existing approaches either employ exploratory analyses that fail to fully leverage the information available in high-dimensional data, or impose a one-dimensional spatial voting model. I derive a clustering algorithm which better captures the probabilistic way in which theories of political behavior conceptualize the swing voter. Building from the canonical finite mixture model, I tailor the model to vote data, for example by allowing uncontested races. I apply this algorithm to actual ballots in the Florida 2000 election and a multi-state survey in 2018. In Palm Beach County, I find that up to 60 percent of voters were straight ticket voters; in the 2018 survey, even higher. The remaining groups of the electorate were likely to cross the party line and split their ticket, but not monolithically: swing voters were more likely to swing for state and local candidates and popular incumbents.

scholarly journals A Greedy Clustering Algorithm Based on Interval Pattern Concepts and the Problem of Optimal Box Positioning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Stepan A. Nersisyan ◽  
Vera V. Pankratieva ◽  
Vladimir M. Staroverov ◽  
Vladimir E. Podolskii
Keyword(s):  
Rate Of Convergence ◽  
Greedy Algorithm ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
Exact Algorithms ◽  
High Dimensional ◽  
High Quality ◽  
Reasonable Time ◽  
Adequate Accuracy ◽  
Clustering Approach

We consider a clustering approach based on interval pattern concepts. Exact algorithms developed within the framework of this approach are unable to produce a solution for high-dimensional data in a reasonable time, so we propose a fast greedy algorithm which solves the problem in geometrical reformulation and shows a good rate of convergence and adequate accuracy for experimental high-dimensional data. Particularly, the algorithm provided high-quality clustering of tactile frames registered by Medical Tactile Endosurgical Complex.

GENERALIZED PARTICLE MODEL USED FOR DATA CLUSTERING

2006 ◽  
Vol 20 (07) ◽  
pp. 1001-1028 ◽  
Author(s):  
DIANXUN SHUAI ◽  
XUE FANGLIANG
Keyword(s):  
Data Clustering ◽  
Large Scale ◽  
Hardware Implementation ◽  
Learning Ability ◽  
High Dimensional ◽  
Particle Model ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Clustering Approach

Data clustering has been widely used in many areas, such as data mining, statistics, machine learning and so on. A variety of clustering approaches have been proposed so far, but most of them are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on a generalized particle model (GPM). The GPM transforms the data clustering process into a stochastic process over the configuration space on a GPM array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, openness and easier hardware implementation with the VLSI systolic technology. The analysis and simulations have shown the effectiveness and good performance of the proposed GPM approach to data clustering.

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