RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base-pair level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and is a critical component in RNA-Seq differential expression analysis. Most existing RNA-Seq quantification tools require the alignments of fragments to either a genome or a transcriptome, entailing a time-consuming and intricate alignment step. In order to improve the performance of RNA-Seq quantification, an alignment-free method, Sailfish, has been recently proposed to quantify transcript abundances using all k-mers in the transcriptome, demonstrating the feasibility of designing an efficient alignment free method for transcriptome quantification. Even though Sailfish is substantially faster than alternative alignment-based methods such as Cufflinks, using all k-mers in the transcriptome quantification impedes the scalability of the method. We proposed a novel RNA-Seq quantification method, RNA-Skim, which partitions the transcriptome into disjoint transcript clusters based on sequence similarity and introduces the notion of sig-mers that are a special type of k-mers uniquely associated with each cluster. We demonstrated that the sig-mer counts within a cluster are sufficient for estimating transcript abundance with an accuracy comparable to any state of the art methods. This enables RNA-Skim to perform transcript quantification on each cluster independently, reducing a complex optimization problem into smaller optimization tasks that can be run in parallel. As a result, RNA-Skim uses less than $4\%$ of the k-mers and less than $10\%$ of the CPU time required by Sailfish. It is able to finish transcriptome quantification in less than 10 minutes per sample by using just a single thread on a commodity computer, which represents more than 100 speedup over the state of the art alignment based methods, while delivering comparable or higher accuracy.