A novel approach to the hierarchical co-clustering in text mining with zone contents and metadata

Abstract: Nowadays, in many text mining applications, eloquent quantity of information from document is present in the form of text. This text information contains distinct types of data such as metadata and zones where metadata can also be called as side information which includes title, name of author, document provenance information, links in the document, user access behavior from web logs and the content of zone can be abstract, body, conclusion etc. It becomes difficult to cluster both the types of information as this text information contains noise which can either improve the aspect of illustration of mining process or can count up noise to the process. As a result of this, there is a need of upright way to carry through mining process so as to increase the superiority of the text information. Co- Clustering discovers clusters of similar objects with regard to the value as well as clusters of similar features with regard to the object associated by them. Conforming to that, this paper represents review on most clustering and co- clustering techniques containing different kinds of data.

Keywords: clustering, coclustering, metadata, text mining, zone.