Sample-Weighted Fuzzy Clustering with Regularizations

Although there have been many researches in cluster
analysis to consider on feature weights, little effort is made on sample
weights. Recently, Yu et al. (2011) considered a probability
distribution over a data set to represent its sample weights and then
proposed sample-weighted clustering algorithms. In this paper, we
give a sample-weighted version of generalized fuzzy clustering
regularization (GFCR), called the sample-weighted GFCR
(SW-GFCR). Some experiments are considered. These experimental
results and comparisons demonstrate that the proposed SW-GFCR is
more effective than the most clustering algorithms.

• Miin-Shen Yang
• Yee-Shan Pan

• Clustering; fuzzy c-means
• fuzzy clustering
• sample weights
• regularization.

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