Mining patterns of dyspepsia symptoms across time points using constraint association rules

Annie Lau; Siew Siew Ong; Ashesh Mahidadia; Achim Hoffmann; Johanna Westbrook; Tatjana Zrimec

Mining patterns of dyspepsia symptoms across time points using constraint association rules

Annie Lau, Siew Siew Ong, Ashesh Mahidadia, Achim Hoffmann, Johanna Westbrook, Tatjana Zrimec

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

Abstract

In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Editors	K.Y. Whang, J. Jeon, K. Shim, J. Srivatava
Place of Publication	Berlin ; London
Publisher	Springer, Springer Nature
Pages	124-135
Number of pages	12
Volume	2637
ISBN (Electronic)	3540047603, 9783540047605
Publication status	Published - 2003
Externally published	Yes
Event	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of Duration: 30 Apr 2003 → 2 May 2003

Other

Other	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	30/04/03 → 2/05/03

Keywords

Association rule with constraints
Domain knowledge
Human interaction
Medical knowledge discovery

Cite this

@inproceedings{1bfb0544a61d47fb91e7e9d75dac597f,

title = "Mining patterns of dyspepsia symptoms across time points using constraint association rules",

abstract = "In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.",

keywords = "Association rule with constraints, Domain knowledge, Human interaction, Medical knowledge discovery",

author = "Annie Lau and Ong, {Siew Siew} and Ashesh Mahidadia and Achim Hoffmann and Johanna Westbrook and Tatjana Zrimec",

year = "2003",

language = "English",

volume = "2637",

pages = "124--135",

editor = "K.Y. Whang and J. Jeon and K. Shim and J. Srivatava",

booktitle = "Advances in Knowledge Discovery and Data Mining",

publisher = "Springer, Springer Nature",

address = "United States",

note = "7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 ; Conference date: 30-04-2003 Through 02-05-2003",

}

Lau, A, Ong, SS, Mahidadia, A, Hoffmann, A, Westbrook, J & Zrimec, T 2003, Mining patterns of dyspepsia symptoms across time points using constraint association rules. in KY Whang, J Jeon, K Shim & J Srivatava (eds), Advances in Knowledge Discovery and Data Mining. vol. 2637, Springer, Springer Nature, Berlin ; London, pp. 124-135, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003, Seoul, Korea, Republic of, 30/04/03.

Mining patterns of dyspepsia symptoms across time points using constraint association rules. / Lau, Annie; Ong, Siew Siew; Mahidadia, Ashesh et al.
Advances in Knowledge Discovery and Data Mining. ed. / K.Y. Whang; J. Jeon; K. Shim; J. Srivatava. Vol. 2637 Berlin ; London: Springer, Springer Nature, 2003. p. 124-135.

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

TY - GEN

T1 - Mining patterns of dyspepsia symptoms across time points using constraint association rules

AU - Lau, Annie

AU - Ong, Siew Siew

AU - Mahidadia, Ashesh

AU - Hoffmann, Achim

AU - Westbrook, Johanna

AU - Zrimec, Tatjana

PY - 2003

Y1 - 2003

N2 - In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.

AB - In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.

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KW - Domain knowledge

KW - Human interaction

KW - Medical knowledge discovery

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SP - 124

EP - 135

BT - Advances in Knowledge Discovery and Data Mining

A2 - Whang, K.Y.

A2 - Jeon, J.

A2 - Shim, K.

A2 - Srivatava, J.

PB - Springer, Springer Nature

CY - Berlin ; London

T2 - 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003

Y2 - 30 April 2003 through 2 May 2003

ER -

Mining patterns of dyspepsia symptoms across time points using constraint association rules

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