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Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes

Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes

Muhammad Usman
Copyright: © 2015 |Pages: 12
ISBN13: 9781466685130|ISBN10: 1466685131|EISBN13: 9781466685147
DOI: 10.4018/978-1-4666-8513-0.ch001
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MLA

Usman, Muhammad. "Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes." Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, IGI Global, 2015, pp. 1-12. https://doi.org/10.4018/978-1-4666-8513-0.ch001

APA

Usman, M. (2015). Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes. In M. Usman (Ed.), Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 1-12). IGI Global. https://doi.org/10.4018/978-1-4666-8513-0.ch001

Chicago

Usman, Muhammad. "Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes." In Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, 1-12. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8513-0.ch001

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Abstract

In high dimensional environments, the sheer size and volume of data poses a number of challenges in order to generate meaningful and informative data cubes. Data cube construction and exploration is a manual process in which analysts are required to visually explore the complex cube structure in order to find interesting information. Data cube construction and exploration has been dealt separately in the literature and in the past there has been very limited amount of work done which would guide the data warehouse designers and analysts to automatically construct and intelligently explore the data cubes. In the recent years, the combined use of data mining techniques and statistical methods has shown promising results in discovering knowledge from large and complex datasets. In this chapter, we propose a methodology that utilizes hierarchical clustering along with Principal Component Analysis (PCA) to generate informative data cubes at different levels of data abstraction. Moreover, automatically ranked cube navigational paths are provided by our proposed methods to enhance knowledge discovery from large data cubes. The methodology has been validated using real world dataset taken from UCI machine learning repository and the results show that the proposed approach assists in cube design and intelligent exploration of interesting cube regions.

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