Discovery of weighted association rules mining

Preetham Kumar; V. S. Ananthanarayana

doi:10.1109/ICCAE.2010.5451339

Discovery of weighted association rules mining

Preetham Kumar, V. S. Ananthanarayana

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

6 Citations (Scopus)

Abstract

Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm.

Original language	English
Title of host publication	2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010
Pages	718-722
Number of pages	5
Volume	5
DOIs	https://doi.org/10.1109/ICCAE.2010.5451339
Publication status	Published - 28-05-2010
Externally published	Yes
Event	2nd International Conference on Computer and Automation Engineering, ICCAE 2010 - Singapore, Singapore Duration: 26-02-2010 → 28-02-2010

Conference

Conference	2nd International Conference on Computer and Automation Engineering, ICCAE 2010
Country/Territory	Singapore
City	Singapore
Period	26-02-10 → 28-02-10

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Control and Systems Engineering

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10.1109/ICCAE.2010.5451339

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title = "Discovery of weighted association rules mining",

abstract = "Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm.",

author = "Preetham Kumar and Ananthanarayana, {V. S.}",

year = "2010",

month = may,

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language = "English",

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volume = "5",

pages = "718--722",

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note = "2nd International Conference on Computer and Automation Engineering, ICCAE 2010 ; Conference date: 26-02-2010 Through 28-02-2010",

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AU - Kumar, Preetham

AU - Ananthanarayana, V. S.

PY - 2010/5/28

Y1 - 2010/5/28

N2 - Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm.

AB - Mining of association rules for basket databases, has been investigated by [1] [3] [4], [9], [12], etc. Most of these works focus on mining binary association rules, i.e, most of the association rules mining algorithms to discover frequent itemsets do not consider the quantity in which items have been purchased. This paper discusses an efficient method for discovering a weighted association rules from a large volumes of data in a single scan of the database. The data structure used here is called Weighted Tree. We found that this algorithm is more efficient than Cai's Algorithm.

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DO - 10.1109/ICCAE.2010.5451339

M3 - Conference contribution

AN - SCOPUS:77952623342

SN - 9781424455850

VL - 5

SP - 718

EP - 722

BT - 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010

T2 - 2nd International Conference on Computer and Automation Engineering, ICCAE 2010

Y2 - 26 February 2010 through 28 February 2010

ER -

Discovery of weighted association rules mining

Abstract

Conference

All Science Journal Classification (ASJC) codes

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