Density Peak Based Clustering Algorithm Research Articles

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BackgroundMajor depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. MethodsHere, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak–based clustering algorithm was used to identify MDD biotypes. ResultsWe found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. ConclusionsOur findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series

Identifying the scale-free interval is an important step in calculating the correlation dimension. In this paper, we propose a method using machine learning known as density peak based clustering algorithm to recognize the scale-free interval. First, the G–P algorithm is used for computing the correlation integral index. Then, the density peak based clustering algorithm is used for classifying the second-order derivative data sets of the correlation integral curve, the zero-fluctuation data are selected to be retained, and then the gross errors are excluded from the selected data. Finally, the coefficient of determination is used to identify the scale-free interval. Some examples are provided to verify the proposed method effective. The calculated results show that our method is feasible. In addition, this research proposes a new method to identify the scale-free interval for fractional dimension calculation theory.

Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network

Precise traffic demand prediction could help government and enterprises make better management and operation decisions by providing them with data-driven insights. However, it is a nontrivial effort to design an effective traffic demand prediction method due to the spatial and temporal characteristics of traffic demand distributions, dynamics of human mobility, and impacts of multiple environmental factors. To handle these problems, a Dynamic Transition Convolutional Neural Network (DTCNN) is proposed for the purpose of precise traffic demand prediction. Particularly, a transition network is first constructed according to the citiwide historical departure and arrival records, where the nodes are virtual stations discovered by a density-peak based clustering algorithm and the edges of two nodes correspond to transition flows of two stations. Then, a dynamic transition convolution unit is designed to model the spatial distributions of the traffic demands, and to capture the evolution of the demand dynamics. Last, a unifying learning framework is provided to incorporate the spatiotemporal states of the traffic demands with environmental factors. Experiments have been conducted on NYC taxi and bike-sharing data, and the results validate the effectiveness of the proposed method.

HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy

Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved in these methods are often difficult to determine. Clustering using a fast search of density peaks is a promising technique for solving this problem. However, the current methods suffer from the problem of uneven distribution within local clusters. To solve this problem, we propose a new density peak based clustering algorithm employing a hierarchical strategy, namely, HCFS, which consists mainly of two stages. In the first stage, the HCFS estimates the density and distance of each point. The points with higher density and distance are selected as candidate centers, and then subclusters centered on them are further obtained. In the second stage, considering that adjacent subclusters based on certain candidate centers are highly similar and connected within the same cluster, we propose a new mechanism for measuring dissimilarity and connectivity between the subclusters. Those highly similar and connected subclusters are merged to increase the dissimilarity between different clusters and to obtain the final clustering results. The experiments conducted on a large number of datasets show that our method can effectively identify unevenly distributed clusters and yield better or comparable performance for different datasets.

Identifying the linear region based on machine learning to calculate the largest Lyapunov exponent from chaotic time series.

To reduce the error caused by the human factor, this paper proposes a modification of a well-known small data method to obtain the largest Lyapunov exponent more accurately, which is based on machine learning for better identification of linear region. Firstly, we use the k-d tree neighborhood search algorithm to improve the computational efficiency of the average divergence index data. Secondly, the unsaturated data are obtained by the density peak based clustering algorithm from the average divergence index data. Thirdly, we use the density peak based clustering algorithm to identify the linear region from the first-order difference curve of the retained data. Finally, the largest Lyapunov exponent is obtained by using the least squares method to fit the linear region. Our method is applied to simulate five famous theoretical chaotic systems, the results show that the proposed method can automatically identify the linear region, which is more accurate than the small data method for the largest Lyapunov exponent calculation and the effectiveness of our method is verified through the simulation of two real-world time series.