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A novel pre-processing method to reduce noise effects in a prototype-based clustering algorithm

Taghikhaki, Z ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. Publisher: 2008
  3. Abstract:
  4. In this paper we introduce a preprocessing method to reduce noise effects in noise prone environments. Prototype based clustering algorithms are sensitive to noise because the effect of noisy data are as same as effect of true data and this affects on calculation of clusters center and then reduces accuracy. Therefore, these algorithms can not be applied in noise-prone environments and if this is applied there, we can not trust to the results. To overcome such problems we reduce and in some cases eliminate the noisy data. Also a part of our method is applied on the source of generated data in a network. Then noisy data that the number of them is high in noisy environments are eliminated and then they aren't transmitted to the center. Therefore the generated traffic and resource consuming are reduced. This can affect seriously on the resource constrained network such as wireless sensor networks. Then it can prolong lifetime of such networks. We discuss and simulate our method on K-means which is a prototype-based clustering algorithm. The results of the simulation demonstrate and validate the efficiency of our proposed method
  5. Keywords:
  6. K-means ; Noise ; Prototype-based clustering algorithm ; Reputation ; Trust ; Information management ; Knowledge engineering ; Wireless sensor networks ; Clustering algorithms
  7. Source: 2008 International Conference on Information and Knowledge Engineering, IKE 2008, Las Vegas, NV, 14 July 2008 through 17 July 2008 ; July , 2008 , Pages 587-593 ; 1601320752 (ISBN); 9781601320759 (ISBN)
  8. URL: https://www.nature.com/articles/s41598-021-93244-2