DNA motif discovery is an important task since it helps to better understand the regulation of the transcription in the protein synthesis process. This paper introduces a novel method for the task of DNA motif finding where the proposed method adopts machine-learning approach by the use of a well-known clustering algorithm, Fuzzy C-Means. The method is explained in detail and tested against DNA sequences extracted from the genome of Saccharomyces cerevisiae and Escherichia coli organisms. Experimental results suggest that the algorithm is efficient in finding statistically interesting features existing in the DNA sequences. The comparison of the algorithm with the well-known motif finding tools, MEME and MDScan, which are built on statistical and word-enumerative models, shows the advantages of the proposed method over the existing tools and the promising direction of the machine-learning approach.