On a two-stage progressive clustering algorithm with graph-augmented density peak clustering

Xinzheng Niu, Yunhong Zheng, Wuji Liu, Chase Q. Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the rapidly growing volume and velocity of big data, real-time streaming data analysis has become increasingly important in many applications. To discover knowledge from such data, a wide range of machine learning techniques have been proposed and used in practice. Among them, clustering, which aims at grouping objects into different classes on the basis of their similarity, is the most common form of unsupervised learning. However, most existing clustering algorithms are designed for static data, and hence are not best suited for streaming data. In this paper, we propose PC-DPC, a two-stage progressive clustering algorithm with graph-augmented density peak clustering. PC-DPC first identifies clusters of streaming data using an improved density peak clustering algorithm, and then merges newly arriving data into the existing data pool by measuring inter-cluster structural similarity, which considers the distance between a center and representative points. We illustrate the superiority of PC-DPC over several state-of-the-art clustering algorithms in terms of clustering accuracy and running time on publicly available benchmark datasets.

Original languageEnglish (US)
Article number104566
JournalEngineering Applications of Artificial Intelligence
Volume108
DOIs
StatePublished - Feb 2022

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Keywords

  • Progressive clustering
  • Streaming data
  • Structural similarity

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