Unsupervised clustering technique to harness ideas from an Ideas Portal

Supervised learning techniques have long been used to analyze unstructured natural language text documents. However, supervised learning techniques are not only computationally intensive but also often require large training corpora. Supervised techniques often fail when such training corpora is either (a) not available or (b) when available, is not statistically significant to enable learning. In many practical scenarios, unsupervised learning techniques become de-facto since the training corpus is not available. In this paper we first describe an unsupervised text analysis technique and demonstrate its usefulness in addressing a real life application to harness ideas from aggregating ideas posted on our company Ideas Portal website.