A Data Cleansing Method for Clustering Large-Scale Transaction Databases
Summary :
In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.11 pp.3120-3123
- Publication Date
- 2010/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.3120
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
Authors
Keyword
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Woong-Kee LOH, Yang-Sae MOON, Jun-Gyu KANG, "A Data Cleansing Method for Clustering Large-Scale Transaction Databases" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 11, pp. 3120-3123, November 2010, doi: 10.1587/transinf.E93.D.3120.
Abstract: In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3120/_p
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@ARTICLE{e93-d_11_3120,
author={Woong-Kee LOH, Yang-Sae MOON, Jun-Gyu KANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Data Cleansing Method for Clustering Large-Scale Transaction Databases},
year={2010},
volume={E93-D},
number={11},
pages={3120-3123},
abstract={In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.},
keywords={},
doi={10.1587/transinf.E93.D.3120},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - A Data Cleansing Method for Clustering Large-Scale Transaction Databases
T2 - IEICE TRANSACTIONS on Information
SP - 3120
EP - 3123
AU - Woong-Kee LOH
AU - Yang-Sae MOON
AU - Jun-Gyu KANG
PY - 2010
DO - 10.1587/transinf.E93.D.3120
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2010
AB - In this paper, we emphasize the need for data cleansing when clustering large-scale transaction databases and propose a new data cleansing method that improves clustering quality and performance. We evaluate our data cleansing method through a series of experiments. As a result, the clustering quality and performance were significantly improved by up to 165% and 330%, respectively.
ER -
English
