Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Mining Allocating Patterns in Investment Portfolios

Mining Allocating Patterns in Investment Portfolios
View Sample PDF
Author(s): Yanbo J. Wang (University of Liverpool, UK), Xinwei Zheng (University of Durham, UK) and Frans Coenen (University of Liverpool, UK)
Copyright: 2009
Pages: 28
Source title: Database Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Erickson (University of Nebraska, Omaha, USA)
DOI: 10.4018/978-1-60566-058-5.ch159


View Mining Allocating Patterns in Investment Portfolios on the publisher's website for pricing and purchasing information.


An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction-database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Related Content

. © 2019. 19 pages.
. © 2019. 44 pages.
. © 2019. 23 pages.
. © 2019. 18 pages.
. © 2019. 11 pages.
. © 2019. 18 pages.
. © 2019. 31 pages.
Body Bottom