2010•Unpublished venueRequires access

Mining High Transaction-Weighted Utility Itemsets

Guo-Cheng Lan, Tzung‐Pei Hong, Vincent S. M. Tseng

Open publisher page 5 citations

Abstract

In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show the proposed approach has a good performance.

About this research paper

What this paper is about

In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show the proposed approach has a good performance.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show the proposed approach has a good performance.

Key concepts: Database transaction, Computer science, Data mining, Transaction processing, Transaction data, Online transaction processing, Database

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