Frequent Document Mining Approach through Clustering

Authors: R K Soni

DOI: https://doi.org/10.17605/OSF.IO/FZ3UK

Short DOI: https://doi.org/ggrs8d

Country:

Full-text Research PDF File:   View   |   Download

Abstract: Now days, finding the association rule from large number of item-set become very popular issue in the field of data mining. To determine the association rule researchers implemented a lot of algorithms and techniques. FP-Growth is a very fast algorithm for finding frequent item-set. This paper, give us a new idea in this field. It replaces the role of frequent item-set to frequent sub graph discovery. It uses the processing of datasets and describes modified FP-algorithm for sub-graph discovery. The document clustering is required for this work. It can use self-similarity function between pair of document graph that similarity can use for clustering with the help of affinity propagation and efficiency of algorithm can be measure by F-measure function.

Keywords: Clustering, Document-graph, FP-growth, Graph Mining, Frequent Sub-graphs Clustering

Paper Id: 12

Published On: 2013-11-28

Published In: Volume 1, Issue 2, November-December 2013

Cite This: Frequent Document Mining Approach through Clustering - R K Soni - IJIRMPS Volume 1, Issue 2, November-December 2013. DOI 10.17605/OSF.IO/FZ3UK

Share this