Supervised Semi-definite Embedding for Email Data Cleaning and Visualization

Ning Liu; Fengshan Bai; Tun Yan; Benyu Zhang; Zheng Chen; Wei-Ying Ma

doi:10.1007/978-3-540-31849-1_93

Supervised Semi-definite Embedding for Email Data Cleaning and Visualization

Ning Liu
, Fengshan Bai
, Tun Yan
, Benyu Zhang
, Zheng Chen
, Wei-Ying Ma

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

3 Citations (Scopus)

Abstract

The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm. © Springer-Verlag Berlin Heidelberg 2005.

Original language	English
Pages (from-to)	972-982
Journal	Lecture Notes in Computer Science
Volume	3399
DOIs	https://doi.org/10.1007/978-3-540-31849-1_93
Publication status	Published - 2005
Externally published	Yes
Event	7th Asia-Pacific Web Conference on Web Technologies Research and Development - APWeb 2005 - Shanghai, China Duration: 29 Mar 2005 → 1 Apr 2005

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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title = "Supervised Semi-definite Embedding for Email Data Cleaning and Visualization",

abstract = "The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.",

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note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 7th Asia-Pacific Web Conference on Web Technologies Research and Development - APWeb 2005 ; Conference date: 29-03-2005 Through 01-04-2005",

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TY - JOUR

T1 - Supervised Semi-definite Embedding for Email Data Cleaning and Visualization

AU - Liu, Ning

AU - Bai, Fengshan

AU - Yan, Tun

AU - Zhang, Benyu

AU - Chen, Zheng

AU - Ma, Wei-Ying

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2005

Y1 - 2005

N2 - The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm. © Springer-Verlag Berlin Heidelberg 2005.

AB - The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm. © Springer-Verlag Berlin Heidelberg 2005.

UR - https://www.scopus.com/pages/publications/24144433445

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-24144433445&origin=recordpage

U2 - 10.1007/978-3-540-31849-1_93

DO - 10.1007/978-3-540-31849-1_93

M3 - RGC 21 - Publication in refereed journal

SN - 0302-9743

VL - 3399

SP - 972

EP - 982

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 7th Asia-Pacific Web Conference on Web Technologies Research and Development - APWeb 2005

Y2 - 29 March 2005 through 1 April 2005

ER -

Supervised Semi-definite Embedding for Email Data Cleaning and Visualization

Abstract

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