Partition clustering for GIS map data protection

Ahmed Abubahia; Mihaela Cocea

doi:10.1109/ICTAI.2014.128

Partition clustering for GIS map data protection

Ahmed Abubahia, Mihaela Cocea

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

174 Downloads (Pure)

Abstract

One of the main research issues of digital data is defined by copyright protection, and digital watermarking is a potential solution to this issue. While there is an abundance of research on digital watermarking for image data, there is far less research on digital watermarking for vector map data, a data format used to store complex information in Geographical Information Systems (GIS). Recently, data mining methods have been used in the process of watermarking vector data. In this paper, we argue that the security of the watermarked vector maps can be increased by employing more suitable data mining methods. In particular, in this paper, we advocate the use of kmedoids partition clustering and compare its deployment with a previous watermarking scheme in which k-means partition clustering is used. The experimental results show that it outperforms the approach based on k-means according to a set of evaluation metrics.

Original language	English
Title of host publication	2014 IEEE 26th international conference on tools with artificial intelligence
Subtitle of host publication	ICTAI 2014 10-12 November 2014, Limassol, Cyprus
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	830-837
ISBN (Print)	9781479965724
DOIs	https://doi.org/10.1109/ICTAI.2014.128
Publication status	Published - 2014
Event	IEEE 26th International Conference on Tools with Artificial Intelligence - Limassol, Cyprus Duration: 10 Nov 2014 → 12 Nov 2014

Conference

Conference	IEEE 26th International Conference on Tools with Artificial Intelligence
Abbreviated title	ICTAI 2014
Country/Territory	Cyprus
City	Limassol
Period	10/11/14 → 12/11/14

Keywords

Clustering algorithms
Clustering methods
Geographic information systems
Robustness
Vectors
Watermarking
ESRI shapefile
GIS
copyright protection
digital watermarking
k-medoids partition clustering
vector data

Access to Document

10.1109/ICTAI.2014.128

ICTAI2014Accepted author manuscript (Post-print), 546 KB

Cite this

@inproceedings{bbf3506952e841a59392ab5785ddd469,

title = "Partition clustering for GIS map data protection",

abstract = "One of the main research issues of digital data is defined by copyright protection, and digital watermarking is a potential solution to this issue. While there is an abundance of research on digital watermarking for image data, there is far less research on digital watermarking for vector map data, a data format used to store complex information in Geographical Information Systems (GIS). Recently, data mining methods have been used in the process of watermarking vector data. In this paper, we argue that the security of the watermarked vector maps can be increased by employing more suitable data mining methods. In particular, in this paper, we advocate the use of kmedoids partition clustering and compare its deployment with a previous watermarking scheme in which k-means partition clustering is used. The experimental results show that it outperforms the approach based on k-means according to a set of evaluation metrics.",

keywords = "Clustering algorithms, Clustering methods, Geographic information systems, Robustness, Vectors, Watermarking, ESRI shapefile, GIS, copyright protection, digital watermarking, k-medoids partition clustering, vector data",

author = "Ahmed Abubahia and Mihaela Cocea",

year = "2014",

doi = "10.1109/ICTAI.2014.128",

language = "English",

isbn = "9781479965724",

pages = "830--837",

booktitle = "2014 IEEE 26th international conference on tools with artificial intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014 ; Conference date: 10-11-2014 Through 12-11-2014",

}

Abubahia, A & Cocea, M 2014, Partition clustering for GIS map data protection. in 2014 IEEE 26th international conference on tools with artificial intelligence: ICTAI 2014 10-12 November 2014, Limassol, Cyprus. Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 830-837, IEEE 26th International Conference on Tools with Artificial Intelligence, Limassol, Cyprus, 10/11/14. https://doi.org/10.1109/ICTAI.2014.128

Partition clustering for GIS map data protection. / Abubahia, Ahmed; Cocea, Mihaela.
2014 IEEE 26th international conference on tools with artificial intelligence: ICTAI 2014 10-12 November 2014, Limassol, Cyprus. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2014. p. 830-837.

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

TY - GEN

T1 - Partition clustering for GIS map data protection

AU - Abubahia, Ahmed

AU - Cocea, Mihaela

PY - 2014

Y1 - 2014

N2 - One of the main research issues of digital data is defined by copyright protection, and digital watermarking is a potential solution to this issue. While there is an abundance of research on digital watermarking for image data, there is far less research on digital watermarking for vector map data, a data format used to store complex information in Geographical Information Systems (GIS). Recently, data mining methods have been used in the process of watermarking vector data. In this paper, we argue that the security of the watermarked vector maps can be increased by employing more suitable data mining methods. In particular, in this paper, we advocate the use of kmedoids partition clustering and compare its deployment with a previous watermarking scheme in which k-means partition clustering is used. The experimental results show that it outperforms the approach based on k-means according to a set of evaluation metrics.

AB - One of the main research issues of digital data is defined by copyright protection, and digital watermarking is a potential solution to this issue. While there is an abundance of research on digital watermarking for image data, there is far less research on digital watermarking for vector map data, a data format used to store complex information in Geographical Information Systems (GIS). Recently, data mining methods have been used in the process of watermarking vector data. In this paper, we argue that the security of the watermarked vector maps can be increased by employing more suitable data mining methods. In particular, in this paper, we advocate the use of kmedoids partition clustering and compare its deployment with a previous watermarking scheme in which k-means partition clustering is used. The experimental results show that it outperforms the approach based on k-means according to a set of evaluation metrics.

KW - Clustering algorithms

KW - Clustering methods

KW - Geographic information systems

KW - Robustness

KW - Vectors

KW - Watermarking

KW - ESRI shapefile

KW - GIS

KW - copyright protection

KW - digital watermarking

KW - k-medoids partition clustering

KW - vector data

U2 - 10.1109/ICTAI.2014.128

DO - 10.1109/ICTAI.2014.128

M3 - Conference contribution

SN - 9781479965724

SP - 830

EP - 837

BT - 2014 IEEE 26th international conference on tools with artificial intelligence

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - IEEE 26th International Conference on Tools with Artificial Intelligence

Y2 - 10 November 2014 through 12 November 2014

ER -

Partition clustering for GIS map data protection

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this