We consider the problem of predicting alternative splicing patterns from a set of expressed sequences (cDNAs and ESTs). Some of these expressed sequences may be errorous, thus forming incorrect exons/introns. These incorrect exons/introns may cause a lot of false positives. For example, we examined a popular alternative splicing database, ECgene, which predicts alternate splicing patterns from expressed sequences. The result shows that about 81.3%-81.6% (sensitivity) of known patterns are found, but the specificity can be as low as 5.9%. Based on the idea that errorous sequences are usually not consistent with other sequences, in this paper we provide an alternative approach for finding alternative splicing patterns which ensures that individual exons/introns of the reported patterns have enough support from the expressed sequences. On the same dataset, our approach can achieve a much higher specificity and a slight increase in sensitivity (38.9% and 84.9%, respectively). Our approach also gives better results compared with popular alternative splicing databases (ASD, ECgene, SpliceNest) and the software ClusterMerge.