We present a novel segmentation approach for Phrase-Based Statistical Machine Translation (PB-SMT) to languages where word boundaries are not obviously marked by using both monolingual and bilingual information and demonstrate that (1) unsegmented corpus is able to provide the nearly identical result compares to manually segmented corpus in PB-SMT task when a good heuristic character clustering algorithm is applied on it, (2) the performance of PB-SMT task has significantly increased when bilingual information are used on top of monolingual segmented result. Our technique, instead of focusing on word separation, mainly concentrate on character clustering. First, we cluster each character from the unsegmented monolingual corpus by employing character co-occurrence statistics and orthographic insight. Secondly, we enhance the segmented result by incorporating the bilingual information which are character cluster alignment, co-occurrence frequency and alignment confidence into that result. We evaluate the effectiveness of our method on PB-SMT task using English-Thai language pair and report the best improvement of 8.1% increase in BLEU score. There are two main advantages of our approach. First, our method requires less effort on developing the corpus and can be applied to unsegmented corpus or poor-quality manually segmented corpus. Second, this technique does not only limited to specific language pair but also capable of automatically adjust the character cluster boundaries to be suitable for other language pairs.