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Activity Number: 378
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #317780
Title: A Computationally Enhanced Fuzzy Clustering Method for Big Biomedical Data
Author(s): Chanpaul Jin Wang* and Hua Fang and Honggang Wang
Companies: University of Massachusetts Medical School and University of Massachusetts Medical School and University of Massachusetts Dartmouth
Keywords: fuzzy clustering ; local density ; directed-acycline-graph ; big biomedical data
Abstract:

Fuzzy C means (FCM) has been widely used in biomedical research, however, its random initialization is a debating feature, and may lead to undesired steady states. Especially, the randomly selected initial centroids may locate far from the final converged centroids, therefore it may not be computationally efficient with large datasets. We propose a computationally enhanced Fuzzy clustering method (eFC) based on the data distribution density. The data density is used to guide the initialization of FCM, and improve its computational performance. The idea is to select the data points near the high-density area as the initial cluster centroids. Specifically, a directed-acycline-graph (DAG) structure is developed to describe the data distributions, and a prune-merge method is applied to locate the initial cluster centroids. Based on the real data and the simulation results, eFC seems to significantly improve the computation performance of FCM especially for large datasets.


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