scholarly journals Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

2020 ◽  
Vol 49 (3) ◽  
pp. 395-411
Author(s):  
Qiannan Wu ◽  
Qianqian Zhang ◽  
Ruizhi Sun ◽  
Li Li ◽  
Huiyu Mu ◽  
...  
Keyword(s):  
High Dimension ◽  
Clustering Algorithm ◽  
Local Density ◽  
Nearest Neighbors ◽  
Allocation Strategy ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Density Peak ◽  
Real World Datasets ◽  
Density Peak Clustering

Cluster analysis plays a crucial component in consumer behavior segment. The density peak clustering algorithm (DPC) is a novel density-based clustering method. However, it performs poorly in high-dimension datasets and the local density for boundary points. In addition, its fault tolerance is affected by one-step allocation strategy. To overcome these disadvantages, an adaptive density peak clustering algorithm based on dimensional-free and reverse k-nearest neighbors (ERK-DPC) is proposed in this paper. First, we compute Euler cosine distance to obtain the similarity of sample points in high-dimension datasets. Then, the adaptive local density formula is used to measure the local density of each point. Finally, the reverse k-nearest neighbor idea is added on two-step allocation strategy, which assigns the remaining points accurately and effectively. The proposed clustering algorithm is experiments on several benchmark datasets and real-world datasets. By comparing the benchmarks, the results demonstrate that the ERK-DPC algorithm superior to some state-of- the-art methods.

scholarly journals A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenni Jiang ◽  
Xiyu Liu ◽  
Minghe Sun
Keyword(s):  
Shannon Entropy ◽  
Clustering Algorithm ◽  
P Systems ◽  
P System ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Density Peak ◽  
New Variant ◽  
Real World Datasets ◽  
Density Peak Clustering

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

scholarly journals Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2014
Author(s):  
Yi Lv ◽  
Mandan Liu ◽  
Yue Xiang
Keyword(s):  
Prior Knowledge ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Density Peak ◽  
Adaptive Clustering ◽  
Clustering Center ◽  
Density Peak Clustering ◽  
Shared Nearest Neighbor ◽  
Fast Searching

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.

scholarly journals Accelerating Density Peak Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 859 ◽  
Author(s):  
Lin
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Early Stage ◽  
Separation Distance ◽  
Density Peak ◽  
Density Peaks ◽  
Density Based Clustering ◽  
Data Points ◽  
Data Point ◽  
Density Peak Clustering

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm.

scholarly journals A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yaohui Liu ◽  
Dong Liu ◽  
Fang Yu ◽  
Zhengming Ma
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Clustering Algorithms ◽  
Real Data ◽  
Nearest Neighbors ◽  
Clustering Methods ◽  
Density Peak ◽  
Hierarchical Clustering Algorithm

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

scholarly journals Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qi Diao ◽  
Yaping Dai ◽  
Qichao An ◽  
Weixing Li ◽  
Xiaoxue Feng ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Local Density ◽  
Search Algorithm ◽  
Real Data ◽  
Nearest Neighbors ◽  
Adjusted Rand Index ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Density Peaks

This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes. Most of the conventional clustering approaches work only with round-shaped clusters. This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy. To overcome these limitations, two improvements are proposed in this paper. To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN). A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph. Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out. Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI).

scholarly journals Clustering with Missing Features: A Density-Based Approach

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Kun Gao ◽  
Hassan Ali Khan ◽  
Wenwen Qu
Keyword(s):  
Real World ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Distance Matrix ◽  
Density Peak ◽  
Density Clustering ◽  
Real World Datasets ◽  
Density Peak Clustering ◽  
Feature Values

Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation methods used for density clustering cause the aggregation phenomenon. To solve this problem, a two-stage novel density peak clustering approach with missing features is proposed: First, the density peak clustering algorithm is used for the data with complete features, while the labeled core points that can represent the whole data distribution are used to train the classifier. Second, we calculate a symmetrical FWPD distance matrix for incomplete data points, then the incomplete data are imputed by the symmetrical FWPD distance matrix and classified by the classifier. The experimental results show that the proposed approach performs well on both synthetic datasets and real datasets.

scholarly journals A Novel Density Peaks Clustering Algorithm Based on K Nearest Neighbors With Adaptive Merging Strategy

2020 ◽  
Author(s):  
Xiaoning Yuan ◽  
Hang Yu ◽  
Jun Liang ◽  
Bing Xu
Keyword(s):  
Real World ◽  
Clustering Algorithm ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Real World Datasets ◽  
Merging Strategy ◽  
Selection Of

Abstract Recently the density peaks clustering algorithm (dubbed as DPC) attracts lots of attention. The DPC is able to quickly find cluster centers and complete clustering tasks. And the DPC is suitable for many clustering tasks. However, the cutoff distance 𝑑𝑑𝑐𝑐 is depends on human experience which will greatly affect the clustering results. In addition, the selection of cluster centers requires manual participation which will affect the clustering efficiency. In order to solve these problem, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (dubbed as KNN-ADPC). We propose a clusters merging strategy to automatically aggregate the over-segmented clusters. Additionally, the K nearest neighbors is adopted to divide points more reasonably. The KNN-ADPC only has one parameter and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove the higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC and DPC-KNN.

scholarly journals A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy

2021 ◽  
Author(s):  
Xiaoning Yuan ◽  
Hang Yu ◽  
Jun Liang ◽  
Bing Xu
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Density Peaks ◽  
Data Points ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Real World Datasets ◽  
Merging Strategy ◽  
Selection Of

AbstractRecently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance $${d}_{c}$$ d c largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

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