Using possibilistic clustering method in microarray dataset of Alzheimer

Microarray technology enables the measurement of a large number of genes or proteins under a specific condition in a short period of time. To make better use of the large amount of information provided by microarray data, biologically clustering of data has been recommended. Due to the high dimensions, noise and redundancy in the microarray data set, it is necessary to use robust clustering approaches. Accordingly, in this paper, to overcome these characteristics, Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM) clustering approaches have been used to deal with noise, uncertainty and outliers. These approaches are then evaluated, using Alzheimer's microarray datasets and validation indices, which indicates the effectiveness of the approaches, particularly the PCM.

Keywords:

Soft clustering , Fuzzy c-means clustering , Psooibilitic c-means clustering , Microarray data , Alzheimer dataset