Publication Type : Conference Paper
Publisher : 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
Source : 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016
Url : https://ieeexplore.ieee.org/document/7919533
ISBN : 9781509006120
Keywords : Clustering algorithms, concept generation, Concept lattice, Context, Data mining, Dimensionality reduction, Document Clustering, document extraction, document handling, domain specific keyword, FCA, formal concept analysis, Information Retrieval, keyword extraction, Lattices, Matrix decomposition, pattern clustering, Singular value decomposition, Sparse matrices, SVD
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Amrita Innovation & Research
Department : Computer Science
Verified : No
Year : 2016
Abstract : Nowadays Information Retrieval (IR) is difficult because of huge amount of information published on the Internet. So it is very relevant to organize documents based on its content. The proposed work address this issue by generating concepts from the documents and these documents are grouped based on a data mining approach. To generate the concept, keywords are extracted from the documents but the extracted set is very large. So for dimensionality reduction, SVD is applied. This paper proposes a novel approach for document clustering based on Formal Concept Analysis (FCA). Concept generation and dimensionality reduction are the two issues addressed here. FCA approach leads to give a fast searching result based on the domain specific keyword. The test result shows that the dimensionality reduction is attained after applying SVD.
Cite this Research Publication :
Jisha R. C., Hari, S., and Shyba, S., “A novel approach for document extraction based on SVD and FCA”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016
