A privacy‐preserving density peak clustering algorithm in cloud computing

2020 ◽  
Vol 32 (11) ◽  
Author(s):  
Liping Sun ◽  
Shang Ci ◽  
Xiaoqing Liu ◽  
Xiaoyao Zheng ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Density Peak ◽  
Density Peak Clustering

scholarly journals A Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image

2019 ◽  
Vol 1229 ◽  
pp. 012024 ◽  
Author(s):  
Fan Hong ◽  
Yang Jing ◽  
Hou Cun-cun ◽  
Zhang Ke-zhen ◽  
Yao Ruo-xia
Keyword(s):  
Clustering Algorithm ◽  
Mr Image ◽  
Density Peak ◽  
Breast Mr ◽  
Density Peak Clustering

scholarly journals Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering

2019 ◽  
Vol 33 ◽  
pp. 4755-4762 ◽  
Author(s):  
Xiaoyu Qin ◽  
Kai Ming Ting ◽  
Ye Zhu ◽  
Vincent CS Lee
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Nearest Neighbour ◽  
Density Peak ◽  
Density Based Clustering ◽  
New Type ◽  
Density Peak Clustering ◽  
The Impact ◽  
First Time ◽  
Tree Method

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

scholarly journals VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

scholarly journals Optimization of Density Peak Clustering Algorithm Based on OpenMP

2018 ◽  
Vol 13 (3) ◽  
pp. 168-179
Author(s):  
Anbo Qiu ◽  
◽  
Zhuowei Wang
Keyword(s):  
Clustering Algorithm ◽  
Density Peak ◽  
Density Peak Clustering

scholarly journals Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 238 ◽  
Author(s):  
Yanqing Yang ◽  
Kangfeng Zheng ◽  
Chunhua Wu ◽  
Xinxin Niu ◽  
Yixian Yang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Fuzzy Membership ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Training Set ◽  
Density Peak ◽  
Proposed Model ◽  
Density Peak Clustering

Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.

Sign in / Sign up

Export Citation Format

Share Document