@article{(Open Science Index):https://publications.waset.org/pdf/12570, title = {Clustering Unstructured Text Documents Using Fading Function}, author = {Pallav Roxy and Durga Toshniwal}, country = {}, institution = {}, abstract = {Clustering unstructured text documents is an important issue in data mining community and has a number of applications such as document archive filtering, document organization and topic detection and subject tracing. In the real world, some of the already clustered documents may not be of importance while new documents of more significance may evolve. Most of the work done so far in clustering unstructured text documents overlooks this aspect of clustering. This paper, addresses this issue by using the Fading Function. The unstructured text documents are clustered. And for each cluster a statistics structure called Cluster Profile (CP) is implemented. The cluster profile incorporates the Fading Function. This Fading Function keeps an account of the time-dependent importance of the cluster. The work proposes a novel algorithm Clustering n-ary Merge Algorithm (CnMA) for unstructured text documents, that uses Cluster Profile and Fading Function. Experimental results illustrating the effectiveness of the proposed technique are also included.}, journal = {International Journal of Computer and Information Engineering}, volume = {3}, number = {4}, year = {2009}, pages = {888 - 895}, ee = {https://publications.waset.org/pdf/12570}, url = {https://publications.waset.org/vol/28}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 28, 2009}, }