.png){kind=icon}
返回
A distributed approximate nearest neighbors algorithm for efficient large scale mean shift clustering
.png){kind=icon}
OA
AI
DOI：10.1016/j.jpdc.2019.07.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Mean Shift clustering, as a generalization of the well-known k-means clustering, computes arbitrarily shaped clusters as defined as the basins of attraction to the local modes created by the density gradient ascent paths. Despite its potential for improved clustering accuracy, the Mean Shift approach is a computationally expensive method for unsupervised learning. We introduce two contributions aiming to provide approximate Mean Shift clustering, based on scalable procedures to compute the density gradient ascent and cluster labeling, with a linear time complexity, as opposed to the quadratic time complexity for the exact clustering. Both propositions are based on Locality Sensitive Hashing (LSH) to approximate nearest neighbors. When implemented on a serial system, these approximate methods can be used for moderate sized datasets. To facilitate the analysis of Big Data, a distributed implementation, written for the Spark/Scala ecosystem is proposed. An added benefit is that our proposed approximations of the density gradient ascent, when used as a pre-processing step in other clustering methods, can also improve the clustering accuracy of the latter. We present experimental results illustrating the effect of tuning parameters on cluster labeling accuracy and execution times, as well as the potential to solve concrete problems in Big Clustering. (C) 2019 Elsevier Inc. All rights reserved.
Keyword:
Clustering
Gradient ascent
Nearest neighbors
Spark
MapReduce
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:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
分享收藏Interclass testing of object oriented software.png)
Eighth IEEE International Conference on Engineering of Complex Computer Systems, 2002. Proceedings.OAAI
Eighth IEEE International Conference on Engineering of Complex Computer Systems, 2002. Proceedings.IF0
OAAI分享收藏分享收藏
查看更多内容