An XML-Enabled Association Rule Framework

L. Feng; T. Dillon; H. Weigand; E. Chang

doi:10.1007/978-3-540-45227-0_10

An XML-Enabled Association Rule Framework

L. Feng, T. Dillon, H. Weigand, E. Chang

Databases (Former)

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

36 Citations (Scopus)

8 Downloads (Pure)

Abstract

With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.

Original language	English
Title of host publication	Database and Expert Systems Applications
Subtitle of host publication	14th International Conference, DEXA 2003, Prague, Czech Republic, September 1-5, 2003. Proceedings
Editors	Vladimir Marik, Werner Retschitzegger, Olga Stepankova
Pages	88-97
Number of pages	10
ISBN (Electronic)	978-3-540-45227-0
DOIs	https://doi.org/10.1007/978-3-540-45227-0_10
Publication status	Published - Sept 2003
Event	14th International Conference on Database and Expert Systems Applications, DEXA 2003 - Prague, Czech Republic Duration: 1 Sept 2003 → 5 Sept 2003 Conference number: 14

Publication series

Name	Lecture notes in computer science
Volume	2736

Conference

Conference	14th International Conference on Database and Expert Systems Applications, DEXA 2003
Abbreviated title	DEXA
Country/Territory	Czech Republic
City	Prague
Period	1/09/03 → 5/09/03

Keywords

DB-DM: DATA MINING
IR-63502
EWI-7214

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10.1007/978-3-540-45227-0_10

Cite this

Feng, L., Dillon, T., Weigand, H., & Chang, E. (2003). An XML-Enabled Association Rule Framework. In V. Marik, W. Retschitzegger, & O. Stepankova (Eds.), Database and Expert Systems Applications: 14th International Conference, DEXA 2003, Prague, Czech Republic, September 1-5, 2003. Proceedings (pp. 88-97). (Lecture notes in computer science; Vol. 2736). https://doi.org/10.1007/978-3-540-45227-0_10

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title = "An XML-Enabled Association Rule Framework",

abstract = "With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.",

keywords = "DB-DM: DATA MINING, IR-63502, EWI-7214",

author = "L. Feng and T. Dillon and H. Weigand and E. Chang",

year = "2003",

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doi = "10.1007/978-3-540-45227-0_10",

language = "English",

isbn = "978-3-540-40806-2",

series = "Lecture notes in computer science",

pages = "88--97",

editor = "Vladimir Marik and Werner Retschitzegger and Olga Stepankova",

booktitle = "Database and Expert Systems Applications",

note = "14th International Conference on Database and Expert Systems Applications, DEXA 2003, DEXA ; Conference date: 01-09-2003 Through 05-09-2003",

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Feng, L, Dillon, T, Weigand, H & Chang, E 2003, An XML-Enabled Association Rule Framework. in V Marik, W Retschitzegger & O Stepankova (eds), Database and Expert Systems Applications: 14th International Conference, DEXA 2003, Prague, Czech Republic, September 1-5, 2003. Proceedings. Lecture notes in computer science, vol. 2736, pp. 88-97, 14th International Conference on Database and Expert Systems Applications, DEXA 2003, Prague, Czech Republic, 1/09/03. https://doi.org/10.1007/978-3-540-45227-0_10

An XML-Enabled Association Rule Framework. / Feng, L.; Dillon, T.; Weigand, H. et al.
Database and Expert Systems Applications: 14th International Conference, DEXA 2003, Prague, Czech Republic, September 1-5, 2003. Proceedings. ed. / Vladimir Marik; Werner Retschitzegger; Olga Stepankova. 2003. p. 88-97 (Lecture notes in computer science; Vol. 2736).

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

TY - GEN

T1 - An XML-Enabled Association Rule Framework

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AU - Dillon, T.

AU - Weigand, H.

AU - Chang, E.

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N2 - With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.

AB - With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.

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An XML-Enabled Association Rule Framework

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Keywords

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