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Active distance-based clustering using k-medoids

Aghaee, A ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-319-31753-3_21
  3. Publisher: Springer Verlag , 2016
  4. Abstract:
  5. k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with n points into a set of k disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upperbound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to cluster objects and to choose some points actively from each bunch of data and acquire the distances between these prominent points from oracle. Experimental results show that the proposed method using only a small subset of the distances can find proper clustering on many real-world and synthetic datasets
  6. Keywords:
  7. Active k-medoids ; Centroid-based clustering ; Distance-based clustering ; Algorithms ; Data mining ; Active clustering ; Based clustering ; Distance-based ; K-medoids ; K-medoids algorithms ; Pairwise distances ; Synthetic datasets ; Triangle inequality ; Clustering algorithms
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 19 April 2016 through 22 April 2016 ; Volume 9651 , 2016 , Pages 253-264 ; 03029743 (ISSN) ; 9783319317526 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-319-31753-3_21