Integration of Data Mining and Data Warehousing: A Practical Methodology

Usman, Muhammad; Pears, Russel

doi:10.4156/ijact.vol2.issue3.4

Cited by 5 publications

References 34 publications

(50 reference statements)

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Discovering diverse association rules from multidimensional schema

Usman

Pears

Fong

2013

Expert Systems with Applications

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The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.

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Discovering diverse association rules from multidimensional schema

Usman

Pears

Fong

2013

Expert Systems with Applications

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A fuzzy-based methodology for accurate classification and prediction in large datasets

Usman

Asghar

2016

IFS

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

Usman

2015

Improving Knowledge Discovery Through the Integration of Data Mining Techniques

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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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Integration of Data Mining and Data Warehousing: A Practical Methodology

Abstract: The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are

Cited by 5 publications

References 34 publications

Discovering diverse association rules from multidimensional schema

Discovering diverse association rules from multidimensional schema

A fuzzy-based methodology for accurate classification and prediction in large datasets

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

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