A Low-Complexity, Low-Memory Frequent Itemset Mining Algorithm for Transactions With Sorted Items

7 Pages Posted: 20 Jan 2019 Last revised: 16 Aug 2019

See all articles by Chris Thron

Chris Thron

Texas A&M University (TAMU), Central Texas

Khoi Tran

TAMU-CT

Ahmed Ali

Department of Computer Science

Date Written: June 2, 2019

Abstract

In this paper, we present an algorithm for finding frequent itemsets in transaction databases in which items are consistently ordered within the list of transactions. The algorithm is designed to reduce redundant reads from the database, and to minimize storage requirements. The algorithm reads through the items in each transaction one at a time and strategically distributes transaction ID's among sublists, that reduce the size of subsequent searches. The data structures used to store these sublists are simple tables whose sizes can be pre-specified, thus making it unnecessary to allocate memory dynamically (which costs additional computer cycles). The tables contain very little redundant information, so that memory is used very efficiently; and table entries are automatically overwritten when no longer needed.

Keywords: association rules, itemsets, mining, frequent, database, complexity, memory

JEL Classification: C80, C88

Suggested Citation

Thron, Christopher and Tran, Khoi and Ali, Ahmed, A Low-Complexity, Low-Memory Frequent Itemset Mining Algorithm for Transactions With Sorted Items (June 2, 2019). Available at SSRN: https://ssrn.com/abstract=3284587 or http://dx.doi.org/10.2139/ssrn.3284587

Christopher Thron (Contact Author)

Texas A&M University (TAMU), Central Texas ( email )

1001 Leadership Place
Killeen, TX 76549
United States

Khoi Tran

TAMU-CT ( email )

1001 Leadership Place
Killeen, TX 76549
United States

Ahmed Ali

Department of Computer Science ( email )

Ahmed Qassem Street
Bahri, Khartoum
Sudan

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