Open AccessClustering Preserving Projections for High-Dimensional Data
Author(s) -
Weiling Cai,
Honghan Zhou,
Le Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012031
Subject(s) - cluster analysis , computer science , fuzzy clustering , pattern recognition (psychology) , projection (relational algebra) , clustering high dimensional data , artificial intelligence , embedding , data mining , cluster (spacecraft) , single linkage clustering , k medians clustering , correlation clustering , cure data clustering algorithm , algorithm , programming language
In this paper, a novel clustering preserving projection method is presented to retain the cluster (or group) structures hidden in the original data. This method involves two steps: (1) the famous clustering method Fuzzy c-means is utilized to discover the cluster distribution in data; (2) such distribution is preserved by finding a linear embedding that minimizes the intra-cluster compactness in low-dimensional space. The feasibility and effectiveness of the proposed method are demonstrated on UCI dataset and USPS digital handwritten dataset.