.png){kind=icon}
返回
A novel density-based clustering algorithm using nearest neighbor graph
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107206.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Density-based clustering has several desirable properties, such as the abilities to handle and identify noise samples, discover clusters of arbitrary shapes, and automatically discover of the number of clusters. Identifying the core samples within the dense regions of a dataset is a significant step of the density-based clustering algorithm. Unlike many other algorithms that estimate the density of each samples using different kinds of density estimators and then choose core samples based on a threshold, in this paper, we present a novel approach for identifying local high-density samples utilizing the inherent properties of the nearest neighbor graph (NNG). After using the density estimator to filter noise samples, the proposed algorithm ADBSCAN in which A stands for Adaptive performs a DBSCAN-like clustering process. The experimental results on artificial and real-world datasets have demonstrated the significant performance improvement over existing density-based clustering algorithms. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Density-based clustering
Nearest neighbor graph
DBSCAN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.4W
被引数:
4.5W
机构
引用论文
Recovery of vision in fish after optic nerve crush: A behavioral and electrophysiological study
PREAI
分享收藏RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density EstimatesRnn-dbscan: 使用反向最近邻密度估计的基于密度的聚类算法
OAAI
OAAI分享收藏分享收藏An efficient and scalable density-based Clustering algorithm for datasets with complex structures.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏How much can k-means be improved by using better initialization and repeats?通过使用更好的初始化和重复，k均值可以提高多少？PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容