Review on Document Recommender Systems Using Hierarchical Clustering Techniques

Priya Mohite¹, Pravin G Kulurkar²

^1,2Vidharbha Institute of Technology, Nagpur

Abstract

We the humans are surrounded with immense unprecedented wealth of information which are available as documents, database or other resources. The access to this information is difficult as by having the information it is not necessary that it could be searched or extracted by the activity we are using. The search engines available should be also customized to handle such queries, sometime the search engines are also not aware of the information they have within the system. The method known as keyword extraction and clustering is introduced which answers this shortcoming by spontaneously recommending documents that are related to users' current activities. When the communication takes place the important text can be extracted from the conversation and the words extracted are grouped and then are matched with the parts in the document. This method uses Natural Language Processing for extracting of keywords and making the subgroup that is a meaningful statement from the group, another method used is the Hierarchical Clustering for creating clusters form the keywords, here the similarity of two keywords is measured using the Euclidean distance. This paper reviews the various methods for the system.

Keywords

• Natural Language Processing (NLP), Hierarchical Clustering, Euclidean Distance

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Cite this Article as Follows

Priya Mohite, Pravin G Kulurkar. "Review on Document Recommender Systems Using Hierarchical Clustering Techniques" International Conference on Computer Applications (2016): 10-14. Print.