An Improved Initialization Method For Fuzzy C-Means Clustering Using Density Based Approach For Microarray Data
نویسنده
چکیده
An improved initialization method for fuzzy cmeans (FCM) method is proposed which aims at solving the two important issues of clustering performance affected by initial cluster centers and number of clusters. A density based approach is needed to identify the closeness of the data points and to extract cluster center. DBSCAN approach defines ε–neighborhood of a point to determine the core objects. Using the core objects number of clusters and membership matrix of fuzzy c-means clustering algorithm are identified. Experiment depicts that this approach can improve clustering result by determining the correct cluster number and membership matrix which are the initial parameters of FCM method which highly influence the clustering result.
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