Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Data stream clustering over sliding windows generates clusters as the window moves. However, iterative clustering using all data in a window is highly inefficient in terms of memory use and computational load. In this paper, we improve data stream clustering over sliding windows using sliding window...

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Bibliographic Details
Main Authors: Jonghem Youn, Junho Shim, Sang-Goo Lee
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Data stream
k-means clustering
locality-sensitive hashing
sliding window
Online Access:https://ieeexplore.ieee.org/document/8501907/

Internet

https://ieeexplore.ieee.org/document/8501907/

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