Sparse data for document clustering

Ionia Veritawati; Ito Wasito; Mujiono

doi:10.1109/ICoICT.2013.6574546

Sparse data for document clustering

Ionia Veritawati, Ito Wasito, Mujiono

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Document clustering which is a part of text mining framework is used to process models and real data collection of cancer documents into several groups. A vector space model of the documents based on their key phrases are formed and called sparse matrix which contains many zero values. A sparse dimensional reduction and several methods of clustering include K-means, Self Organizing and Non-negative Matrices Factorization (NMF) are applied to the data, then the results are compared. Sparse method in dimensional reduction step using Arnoldi Method provides a better result of clustering validity twice more than standard dimensional reduction result.

Original language	English
Title of host publication	2013 International Conference of Information and Communication Technology, ICoICT 2013
Pages	38-43
Number of pages	6
DOIs	https://doi.org/10.1109/ICoICT.2013.6574546
Publication status	Published - 2013
Event	2013 International Conference of Information and Communication Technology, ICoICT 2013 - Bandung, Indonesia Duration: 20 Mar 2013 → 22 Mar 2013

Publication series

Name	2013 International Conference of Information and Communication Technology, ICoICT 2013

Conference

Conference	2013 International Conference of Information and Communication Technology, ICoICT 2013
Country/Territory	Indonesia
City	Bandung
Period	20/03/13 → 22/03/13

Keywords

arnoldi method
competitive learning
k-means
non-negative matrices factorization
self-organizing
sparse

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICoICT.2013.6574546

Cite this

@inproceedings{b99154ff305d45a79d06e19813f79427,

title = "Sparse data for document clustering",

abstract = "Document clustering which is a part of text mining framework is used to process models and real data collection of cancer documents into several groups. A vector space model of the documents based on their key phrases are formed and called sparse matrix which contains many zero values. A sparse dimensional reduction and several methods of clustering include K-means, Self Organizing and Non-negative Matrices Factorization (NMF) are applied to the data, then the results are compared. Sparse method in dimensional reduction step using Arnoldi Method provides a better result of clustering validity twice more than standard dimensional reduction result.",

keywords = "arnoldi method, competitive learning, k-means, non-negative matrices factorization, self-organizing, sparse",

author = "Ionia Veritawati and Ito Wasito and Mujiono",

year = "2013",

doi = "10.1109/ICoICT.2013.6574546",

language = "English",

isbn = "9781467349925",

series = "2013 International Conference of Information and Communication Technology, ICoICT 2013",

pages = "38--43",

booktitle = "2013 International Conference of Information and Communication Technology, ICoICT 2013",

note = "2013 International Conference of Information and Communication Technology, ICoICT 2013 ; Conference date: 20-03-2013 Through 22-03-2013",

}

Veritawati, I, Wasito, I & Mujiono 2013, Sparse data for document clustering. in 2013 International Conference of Information and Communication Technology, ICoICT 2013., 6574546, 2013 International Conference of Information and Communication Technology, ICoICT 2013, pp. 38-43, 2013 International Conference of Information and Communication Technology, ICoICT 2013, Bandung, Indonesia, 20/03/13. https://doi.org/10.1109/ICoICT.2013.6574546

Sparse data for document clustering. / Veritawati, Ionia; Wasito, Ito; Mujiono.
2013 International Conference of Information and Communication Technology, ICoICT 2013. 2013. p. 38-43 6574546 (2013 International Conference of Information and Communication Technology, ICoICT 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sparse data for document clustering

AU - Veritawati, Ionia

AU - Wasito, Ito

AU - Mujiono,

PY - 2013

Y1 - 2013

N2 - Document clustering which is a part of text mining framework is used to process models and real data collection of cancer documents into several groups. A vector space model of the documents based on their key phrases are formed and called sparse matrix which contains many zero values. A sparse dimensional reduction and several methods of clustering include K-means, Self Organizing and Non-negative Matrices Factorization (NMF) are applied to the data, then the results are compared. Sparse method in dimensional reduction step using Arnoldi Method provides a better result of clustering validity twice more than standard dimensional reduction result.

AB - Document clustering which is a part of text mining framework is used to process models and real data collection of cancer documents into several groups. A vector space model of the documents based on their key phrases are formed and called sparse matrix which contains many zero values. A sparse dimensional reduction and several methods of clustering include K-means, Self Organizing and Non-negative Matrices Factorization (NMF) are applied to the data, then the results are compared. Sparse method in dimensional reduction step using Arnoldi Method provides a better result of clustering validity twice more than standard dimensional reduction result.

KW - arnoldi method

KW - competitive learning

KW - k-means

KW - non-negative matrices factorization

KW - self-organizing

KW - sparse

UR - http://www.scopus.com/inward/record.url?scp=84883482884&partnerID=8YFLogxK

U2 - 10.1109/ICoICT.2013.6574546

DO - 10.1109/ICoICT.2013.6574546

M3 - Conference contribution

AN - SCOPUS:84883482884

SN - 9781467349925

T3 - 2013 International Conference of Information and Communication Technology, ICoICT 2013

SP - 38

EP - 43

BT - 2013 International Conference of Information and Communication Technology, ICoICT 2013

T2 - 2013 International Conference of Information and Communication Technology, ICoICT 2013

Y2 - 20 March 2013 through 22 March 2013

ER -

Sparse data for document clustering

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this