CLUSTERING 3 Descriptive Document Clustering via Discriminant Learning in a Co-embedded Space of Multi-level Similarities

Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarise the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of two types of heterogeneous objects, that correspond to documents and candidate phrases, using multi-level similarity information. CEDL is composed of five main processing stages. Firstly, it simultaneously maps the documents and candidate phrases into a common co-embedded space that preserves higher-order neighbour-based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme utilising multiple scores based on the extracted co-embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multi-topic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields. DESCRIPTIVE DOCUMENT CLUSTERING 3 Descriptive Document Clustering via Discriminant Learning in a Co-embedded Space of Multi-level Similarities