Data Mining — a Rough Set Perspective

Pawlak, Zdzisław

doi:10.1007/3-540-48912-6_3

Cited by 11 publications

(1 citation statement)

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“…Parallel research in granular computing emphasize on data mining on granular structures which are abstract linguistic, natural language formulations on information spaces [15]. The theory gives a computing paradigm where information granule is a clump of objects drawn by indistinguishability, similarity and proximity of functionality [16]- [18]. Qian, Liang, Yao, & Dang extended Pawlak's Rough set model to multi-granulation rough sets.…”

Section: Introductionmentioning

confidence: 99%

Mining Multi-Dimensional Intra and Inter-Association Patterns of Call Records for Targeted Advertising using Multi-Granulation Rough Sets

Bisaria

Pardasani

2019

IJCAI

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Customer contacts to various businesses identified in telecom call records convey their interest in availing those services. Multi-dimensional attribute dependence with day and time of such communications generate useful insights for targeted advertising. Also, frequent and significant inter patterns of service associations give the probability that takers of one service may also be the prospects of the other. This work presents a multi granulation rough sets model to address the issue of prospect discovery from interest traits depicted in call records. The proposed method solves problems like higher computational complexity and large statistically insignificant patterns space inherent in traditional intra and interpattern mining methods. The algorithm is tested to generate target audience for food and restaurant business using one-month data of anonymous call records of a Thailand based telecom service provider. Some interesting mathematical properties of underlying knowledge structures are also validated.Povzetek: Klici v tajsko restavracijo so analizirani z namenom iskanja učinkovitih tržnih vzorcev.

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Section: Introductionmentioning

confidence: 99%