When facing real-world data without ground-truths, it is often challenging and time-consuming to identify better parameter values for parametric clustering algorithms. Considering this, we propose a density peak clustering algorithm guided by pseudo labels (PLDPC), in which the manually pre-specified...
Flexible density peak clustering for real-world data 2024, Pattern Recognition Citation Excerpt : In summary, our algorithm performs better than DSet-DPC in both clustering quality and running time, showing as a better clustering approach. Finally, we compare our algorithm with existing algorithms, ...
In order to perform effective and reasonable clustering on large-scale complex policy sets, we present an optimization algorithm based on the DPCA (Density Peak Clustering Algorithm)12 and GWO (Grey Wolf Optimizer)34, which are explained in detail below. ...
GDPC: generalized density peaks clustering algorithm based on order similarityClusteringOrder similarityDensityDensity peakGraphClustering is a fundamental approach to discover the valuable information in data mining and machine learning. Density peaks clustering is a typical density based clustering and has ...
class DensityPeakCluster(object): """ Density Peak Cluster. Methods: fit: fit model plot: plot clustering Attributes: n_id: data row count distance: each id distance dc: threshold of density cut off rho: each id density nneigh: each id min upper density nearest neighbor delta: each id ...
The density peak points have both high value of \({\rho }_{i}\) and \({\delta }_{i}\).The detail process of DPC algorithm is shown as Algorithm 1.According to Algorithm 1, in Step 2, the space complexity increases significantly while calculating the distance matrix between all data ...
First, the algorithm preprocesses the data using outlier detection methods to identify and remove outliers that may affect subsequent analysis. Then, it employs a density peak clustering method to determine the optimal number of clusters and initial centers. Finally, the algorithm classifies and ...
To overcome these problems, a Main Density Peak Clustering algorithm (MDPC+1)—clustering by fast detection of main density peaks within a peak digraph—is proposed, where a main density peak is the highest density peak in a cluster. MDPC+ can easily detect the real centers of multi-peak ...
distribution in Fig. 2A would not be assigned to any peak. 为了对我们的程序进行基准测试,让我们首先考虑图2中的测试用例。数据点来自具有非球形和强重叠峰的概率分布(图2A);与最大值对应的概率值相差几乎一个数量级。在图2中,B和C中,分别从图2A中的分布中提取了4000个和1000个点。在相应的决策图(图2...
The density peak clustering algorithm treats local density peaks as cluster centers, and groups non-center data points by assuming that one data point and its nearest higher-density neighbor are in the same cluster. While this algorithm is shown to be promising in some applications, its clustering...