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Data Mining Task Optimization with Soft Computing Approach

  • Lokesh GagnaniEmail author
  • Kalpesh Wandra
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)

Abstract

The data mining task optimization (DMTO) is one of the emerging research areas in the branch of data mining. We discuss the data mining tasks as classification and clustering. The optimization of these tasks is done using soft computing methods. The soft computing is also a new term coined for optimization problems. The classification task is achieved using the support vector machine and results optimized with particle swarm optimization and simplified swarm optimization with exchange local strategy (SSO with ELS). The clustering task is achieved using the kernel fuzzy c-means (KFCM) and results optimized using particle swarm optimization (PSO) and intelligent firefly algorithm (IFA). The results of both the tasks are worked out in the same paper. The results obtained outperform the existing approach SVM with PSO with cuckoo search (CS) and PSO with social spider optimization (SSO) for classification, KFCM with PSO with Bacteria Foraging Optimization (BFO) for clustering in data mining task optimization framework.

Keywords

Data mining task optimization Soft computing Classification Clustering Support vector machine Kernel fuzzy c-means 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringC U Shah UniversityWadhwanIndia
  2. 2.Department of Computer EngineeringGMB PolytechnicRajulaIndia

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