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A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark.

Huidong Ling1, Xinmu Zhu1, Tao Zhu1

  • 1School of Computer Science, University of South China, Hengyang 421200, China.

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|February 25, 2023
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Summary
This summary is machine-generated.

This study introduces a parallel multiobjective particle swarm optimization (PSO) clustering algorithm for large datasets. The new approach enhances efficiency on distributed systems like Apache Spark, reducing processing time and improving data distribution.

Keywords:
Apache Sparkmultiobjective clusteringmultiobjective particle swarm optimization (MOPSO)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Existing multiobjective clustering algorithms using particle swarm optimization (PSO) are limited to single machines, hindering scalability for large datasets.
  • Parallel computing frameworks offer data parallelism but can suffer from unbalanced data distribution, impacting clustering accuracy.
  • Efficiently handling large-scale data requires parallelized algorithms that maintain clustering quality.

Purpose of the Study:

  • To propose a parallel multiobjective PSO clustering algorithm optimized for distributed computing environments.
  • To address the challenges of data parallelism, specifically unbalanced data distribution and communication overhead.
  • To enhance the efficiency and scalability of multiobjective clustering for large-scale data analysis.

Main Methods:

  • Developed Spark-MOPSO-Avg, a parallel multiobjective PSO weighted average clustering algorithm leveraging Apache Spark.
  • Implemented data parallelism by partitioning and caching datasets in memory across a distributed cluster.
  • Utilized a weighted average approach for local fitness values to mitigate issues from unbalanced data distribution.

Main Results:

  • The Spark-MOPSO-Avg algorithm demonstrates significant reductions in algorithm time overhead on a Spark distributed cluster.
  • Data parallelism resulted in a minor accuracy loss of approximately 1% to 9% in information loss.
  • The algorithm exhibits good execution efficiency and parallel computing capabilities, suitable for large-scale data.

Conclusions:

  • Spark-MOPSO-Avg effectively reduces processing time and communication overhead in large-scale clustering tasks.
  • The weighted average method successfully addresses unbalanced data distribution challenges in parallel PSO clustering.
  • The proposed algorithm offers a scalable and efficient solution for multiobjective clustering in distributed environments.