Context-aware Document-clustering Technique

Document clustering is an intentional act that should reflect individuals’ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective documentclustering techniques need to take into account a user’s categorization context defined by or relevant to the target task under consideration. However, existing document-clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate context-aware document-clustering. In response, we propose a Context-Aware document-Clustering (CAC) technique that takes into consideration a user’s categorization preference (expressed as a list of anchoring terms) relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. Our empirical evaluation results suggest that our proposed CAC technique outperforms the pure content-based document-clustering technique

Collaborative Filtering-based Context-Aware Document-Clustering (CF-CAC) Technique

Document clustering is an intentional act that should reflect an individual\u27s preference with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user\u27s categorization context. In response, Yang & Wei (2007) propose a Context-Aware document Clustering (CAC) technique that takes into consideration a user\u27s categorization preference relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. However, the CAC technique encounters the problem of small-sized anchoring terms. To overcome this shortcoming, we extend the CAC technique and propose a Collaborative Filtering-based Context-Aware document-Clustering (CF-CAC) technique that considers not only a target user\u27s but also other users\u27 anchoring terms when approximating the categorization context of the target user. Our empirical evaluation results suggest that our proposed CF-CAC technique outperforms the CAC technique