Since you said in the comments that your goal is to remove duplicate reads, I'm going to address that issue. There is no need for clustering. If you are working with a reference genome, then you should map your reads to the reference and consider reads that map to the same genomic coordinate as duplicates. For example, you could use the "samtools rmdup" command. If you do not have a reference genome, then you are most likely going to want to use some kind of de novo assembly program based on de-Bruijn graphs, in which case you would correct sequencing errors at the kmer level, using something like khmer.

For the typical whole-genome or whole-transcriptome applications that Illumina sequencing is typically used for, clustering reads by sequence identity is not likely to produce a useful result, because in most cases the reads are expected to be tiled along much longer fragments of DNA, so a clustering approach would somewhat arbitrarily and randomly segment that longer stretch into poorly-separated "clusters" that each represent roughly one read-length of the larger fragment.