• GREESHMA RG
• SMITHA ES

Abstract-Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications. The clustering problem is defined to be that of finding groups of similar objects in the data. The similarity between the objects is measured with the use of a similarity function. The problem of clustering can be very useful in the text domain, where the objects to be clusters can be of different granularities such as documents, paragraphs, sentences or terms. Clustering is especially useful for organizing documents to improve retrieval and support browsing. Text mining is the analysis of data contained in natural language text. Text databases are rapidly growing due to the increasing amount of information available in various electronic forms. User need to access relevant information across multiple documents. In many text mining applications side information is available along with the text documents. Here side information is referred to as auxiliary attribute. This information corresponds to different kinds of attributes such as the document provenance information, information related to origin of documents etc. Such side information may contain a huge amount of information. This huge amount of information may be used for performing clustering. This paper represents review on most clustering techniques containing different kinds of data.

Classification, Clustering, Data mining, Side information, Text Mining.

" A review on mining text data with auxiliary attributes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 7, page no.3028-3030, July-2015, Available :http://www.jetir.org/papers/JETIR1507007.pdf

" A review on mining text data with auxiliary attributes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 7, page no. pp3028-3030, July-2015, Available at : http://www.jetir.org/papers/JETIR1507007.pdf