Mining Association Rules in Data Warehouses

Tjioe, Haorianto Cokrowijoyo; Taniar, David

doi:10.4018/978-1-59904-951-9.ch020

Cited by 7 publications

(7 citation statements)

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“…Прикладами даного підходу є інтеграція багатомірної інформації в послідовності даних і подальше їх дослідження на виявлення закономірностей [10], згладжування кубів даних і витяг з них матриць для кожного виміру на кожному кроці побудови дерева рішень [11]. Також був запропонований метод пошуку асоціативних правил у сховищах даних, який заснований на організації багатомірних даних і здатний витягувати асоціативні правила з декількох вимірів на різних рівнях абстракції [12].…”

Section: аналіз літературних данихunclassified

Olap and data mining technologies' integration in the construction of interdimensional associative rules in multidimensional data

Фісун¹,

Horban²

2015

ScienceRise

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Представлено особливості пошуку асоціативних правил у багатомірних даних, а саме показані теоретичні засади знаходження асоціацій між різними вимірами у OLAP-кубах та формули обчислення характеристик їх значущості (підтримка, достовірність, ліфт та леверидж). Запропоновано метод генерації міжвимірових асоціативних правил та описано його реалізацію як складової інформаційної системи оперативного та інтелектуального аналізу даних на платформі об'єктної системи керування базами даних Caché

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Section: аналіз літературних данихunclassified

Olap and data mining technologies' integration in the construction of interdimensional associative rules in multidimensional data

Фісун¹,

Horban²

2015

ScienceRise

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“…The absence of repetitive predicates is a limitation on the expressiveness of rules. Other authors have proposed ad-hoc algorithms to extract hybrid rules in which the repetition of few dimensions is enabled [16,18,33]. [17,27,34,36] have studied "How current and past values are related to future values".…”

Section: Related Workmentioning

confidence: 99%

Discovering descriptive rules in relational dynamic graphs

Nguyen

Cerf

Plantevit

et al. 2013

IDA

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Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the so-called relational dynamic oriented graphs that can be encoded as n-ary relations with n 3 and thus represented by Boolean tensors. Two dimensions are used to encode the graph adjacency matrices and at least one other denotes time. We design the pattern domain of multi-dimensional association rules, i.e., non trivial extensions of the popular association rules that may involve subsets of any dimensions in their antecedents and their consequents. First, we design new objective interestingness measures for such rules and it leads to different approaches for measuring the rule confidence. Second, we must compute collections of a priori interesting rules. It is considered here as a post-processing of the closed patterns that can be extracted efficiently from Boolean tensors. We propose optimizations to support both rule extraction scalability and non redundancy. We illustrate the added-value of this new data mining task to discover patterns from a real-life relational dynamic graph.

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“…It is one of the most well studied data mining task used for decision making. It also explores the association among many dimensional data items in the dataset (among different dimensions of data cube) [6] [7]. Datasets extracted from processes log contain information about agent assignments, process contexts and (achieved) business benefits.…”

Section: • [Agent=clara and Context=laptop] [Business Benefit=yes] [1mentioning

confidence: 99%

Agent assignment for process management

Jablonski

Talib

2009

Proceedings of the 7th International Conference on Frontiers of Information Technology

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Workflow Management Systems not only need to provide a mechanism to represent the organizational elements involved during the execution of workflow processes but they also must provide the mechanism for continuous improvements within these elements. But, in almost, all workflow management systems the role concept is determined once at the introduction of a workflow application and is not reevaluated to observe how successfully certain processes are performed by the authorized agents. This paper describes an approach which evaluates whether agents assigned to a process are working successfully and feed this information back for future agent assignments promising best business benefit for the enterprise. The approach is called Agent Performance Evaluation (APE) and is based on data mining technique combined with post processing technique.

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Mining Association Rules in Data Warehouses

Cited by 7 publications

References 0 publications

Olap and data mining technologies' integration in the construction of interdimensional associative rules in multidimensional data

Olap and data mining technologies' integration in the construction of interdimensional associative rules in multidimensional data

Discovering descriptive rules in relational dynamic graphs

Agent assignment for process management

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