The Data Warehouse: A Knowledge Creating Resource?

With the growing interest and development in knowledge discovery in databases (KDD), the data warehouse has been pushed to the forefront as a source of knowledge creation. As new information technologies (ITs), data warehousing technologies have been primarily studied from a technical perspective, and are under- researched from organizational and behavioral perspectives. Hence, the broad objective of this paper is to address the gap in prior work by examining the processes through which organizations successfully deploy and use data warehousing technologies, and how usage can contribute to organizational learning

ITER: An algorithm for predictive regression rule extraction. Data warehousing and knowledge discovery. Proceedings.

Various benchmarking studies have shown that artificial neural networks and support vector machines have a superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the motivation behind these models' decisions. Various rule extraction techniques have been proposed to overcome this opacity restriction. However, most of these extraction techniques are devised for classification and only few algorithms can deal with regression problems.

Sequential Patterns Post-processing for Structural Relation Patterns Mining

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discover

Graph-based Modelling of Concurrent Sequential Patterns

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed