Iterative mining for rules with constrained antecedents

Zheng Sun; Philip S. Yu; Xiang Yang Li

doi:10.1137/1.9781611972757.59

Iterative mining for rules with constrained antecedents

Zheng Sun, Philip S. Yu, Xiang Yang Li

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

In this study we discuss the following association rule mining problem: for a userdefined set A of items, the objective is to compute all association rules (satisfying suitable support and confidence thresholds) induced by A, where an association rule is said to be induced by A if its antecedent (i.e., LHS) is a subset of A while the consequent (i.e., RHS) contains no items in A. In particular, we are interested in a multistep scenario where in each step A is incremented by one item and all association rules induced by the updated A are to be computed. We propose an efficient iterative algorithm that can exploit mining information gained in previous steps to efficiently answer subsequent queries.

Original language	English
Title of host publication	Proceedings of the 2005 SIAM International Conference on Data Mining (SDM)
Editors	Hillol Kargupta, Jaideep Srivastava, Chandrika Kamath, Arnold Goodman
Place of Publication	Philadelphia
Publisher	Society for Industrial and Applied Mathematics (SIAM)
Pages	551-555
Number of pages	5
Edition	1st
ISBN (Electronic)	9781611972757
ISBN (Print)	9780898715934
DOIs	https://doi.org/10.1137/1.9781611972757.59
Publication status	Published - 21 Apr 2005
Externally published	Yes
Event	5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States Duration: 21 Apr 2005 → 23 Apr 2005

Conference

Conference	5th SIAM International Conference on Data Mining, SDM 2005
Country/Territory	United States
City	Newport Beach, CA
Period	21/04/05 → 23/04/05

Scopus Subject Areas

Software

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10.1137/1.9781611972757.59

Cite this

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title = "Iterative mining for rules with constrained antecedents",

abstract = "In this study we discuss the following association rule mining problem: for a userdefined set A of items, the objective is to compute all association rules (satisfying suitable support and confidence thresholds) induced by A, where an association rule is said to be induced by A if its antecedent (i.e., LHS) is a subset of A while the consequent (i.e., RHS) contains no items in A. In particular, we are interested in a multistep scenario where in each step A is incremented by one item and all association rules induced by the updated A are to be computed. We propose an efficient iterative algorithm that can exploit mining information gained in previous steps to efficiently answer subsequent queries.",

author = "Zheng Sun and Yu, {Philip S.} and Li, {Xiang Yang}",

year = "2005",

month = apr,

day = "21",

doi = "10.1137/1.9781611972757.59",

language = "English",

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pages = "551--555",

editor = "Hillol Kargupta and Jaideep Srivastava and Chandrika Kamath and Arnold Goodman",

booktitle = "Proceedings of the 2005 SIAM International Conference on Data Mining (SDM)",

publisher = "Society for Industrial and Applied Mathematics (SIAM)",

address = "United States",

edition = "1st",

note = "5th SIAM International Conference on Data Mining, SDM 2005 ; Conference date: 21-04-2005 Through 23-04-2005",

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Sun, Z, Yu, PS & Li, XY 2005, Iterative mining for rules with constrained antecedents. in H Kargupta, J Srivastava, C Kamath & A Goodman (eds), Proceedings of the 2005 SIAM International Conference on Data Mining (SDM). 1st edn, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, pp. 551-555, 5th SIAM International Conference on Data Mining, SDM 2005, Newport Beach, CA, United States, 21/04/05. https://doi.org/10.1137/1.9781611972757.59

Iterative mining for rules with constrained antecedents. / Sun, Zheng; Yu, Philip S.; Li, Xiang Yang.
Proceedings of the 2005 SIAM International Conference on Data Mining (SDM). ed. / Hillol Kargupta; Jaideep Srivastava; Chandrika Kamath; Arnold Goodman. 1st. ed. Philadelphia: Society for Industrial and Applied Mathematics (SIAM), 2005. p. 551-555.

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - In this study we discuss the following association rule mining problem: for a userdefined set A of items, the objective is to compute all association rules (satisfying suitable support and confidence thresholds) induced by A, where an association rule is said to be induced by A if its antecedent (i.e., LHS) is a subset of A while the consequent (i.e., RHS) contains no items in A. In particular, we are interested in a multistep scenario where in each step A is incremented by one item and all association rules induced by the updated A are to be computed. We propose an efficient iterative algorithm that can exploit mining information gained in previous steps to efficiently answer subsequent queries.

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ER -

Iterative mining for rules with constrained antecedents

Abstract

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Scopus Subject Areas

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