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Mining Allocating Patterns in Investment Portfolios

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posted on 2025-03-03, 04:05 authored by Yanbo J Wang, Xinwei ZhengXinwei Zheng, Frans Coenen
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.

History

Chapter number

159

Pagination

2657-2684

Open access

  • No

ISBN-13

9781605660585

Language

eng

Publication classification

B1.1 Book chapter

Extent

160

Publisher

IGI Global

Place of publication

Hershey, Pa.

Title of book

Database Technologies: Concepts, Methodologies, Tools, and Applications

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