Modification of the Fast Global K-Means Using A Fuzzy Relation With Application in Microarray Data Analysis

Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. in this paper, fast global k-means (fast gkm) is developed for clustering the gene expression datasets. fast gkm is a significant improvement of the k-means clustering method. it is an incremental clustering method which starts with one cluster. iteratively new clusters are added. since in each epoch, all data points are examined for the next cluster center, it is believed that fast gkm attains a near global solution. in the gene expression clustering problem, genes with significant differential expression levels, across the output disease classes, are important for the accurate classification of samples. thus, a fuzzy entropy measure which is designated based on maximum within class and minimum between class relevance is exerted in to the search procedure of the fast gkm. as a result, the search procedure of the proposed method is conducted in such a way to provide clusters which assembles the most discriminative genes closer to their centers. therefore, capacity of the fast gkm which is its ability to find global clusters is managed in a profitable way. to demonstrate the usefulness of the proposed method, three published microarray datasets are used: leukemia, prostate, and colon. classification results are found robust and accurate using three public classification methods: k-nn, svm, and naïve bayesian.