BITS Meetings' Virtual Library:
Abstracts from Italian Bioinformatics Meetings from 1999 to 2013

766 abstracts overall from 11 distinct proceedings

1. Fu L-M, Medico E
FMC, a Fuzzy Map Clustering algorithm for microarray data analysis
Meeting: BITS 2004 - Year: 2004
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Topic: Microarray algorithms and data analysis

Abstract: As the microarray technology is emerging as a widely used tool to investigate gene expression and function, laboratories over the world have produced and are producing a huge amount of data, which demand advanced and specialized computational tools to process them. Clustering methods have been successfully applied to such data to reorganize the data and extract biological information from them. But the classical clustering methods [1] such as k-means and hierarchical clustering have some intrinsic limits such as the linear, pair-wise nature of the similarity metrics (which fail to highlight non-linear substructures of the data) and the univocal assignment of each gene to one cluster (which may fail to highlight cluster-to-cluster relationships) [2]. Here we introduce a novel method for clustering microarray data, named Fuzzy Map Clustering (FMC), which may partly overcome these limits. Basically, the clustering process of FMC starts from identification of an initial set of clusters by calculating the “density” around each data point (object), that is, the average proximity of its K nearest other objects (K neighbours) and choosing the ones that have the highest density among all their K neighbors. K can be a fixed number of choice or the number of neighbors within a distance threshold. Then, each object in the dataset is assigned a fuzzy membership to all the defined clusters (a vector containing a percentage of membership to all the clusters). Membership is assigned so that similar objects have similar fuzzy membership vectors. Membership assignment is optimized by measuring how the fuzzy membership vector of one object can be approximated by the vectors of its neighbors. Finally, a process based on the merging of adjacent clusters and fuzzy membership reassignment is reiterated until the number of clusters is reduced to a fixed one decided by the operator. Our computational experiments have shown that FMC can correctly reveal the true cluster structure of the dataset if such structure exists, even if the clusters contained in the dataset have arbitrary shape. And perhaps the basic idea underlying FMC points out a new way to develop novel clustering methods with good mathematical foundation.

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