An approach of clustering data with noisy or imprecise feature measurement
In this paper we considered clustering of data corrupted by noise or suffering from imprecision due to finite resolution of the feature measuring device. Our work is motivated by the fact that no measurement can be made perfect and addition of noise is not an uncommon phenomenon for telemetric data. Here we tried to show how the classical k-means algorithm should be modified to take care of the noise/imprecision. Experimental results on Fisher's Iris data and a Nutrition data are demonstrated.
robust clustering noisy data imprecise data k-means clustering fuzzy clustering