Using Data Cube for Mining of Hybrid-Dimensional Association Rules

Using Data Cubes for Metarule-Guided Mining of Multi-Dimensional Association Rules

Metarule-guided mining is an interactive approach to data mining, where users probe the data under analysis by specifying hypotheses in the form of metarules, or pattern templates. Previous methods for metarule-guided mining of association rules have primarily used a transac-tion/relation table-based structure. Such approaches require costly, multiple scans of the data in order to nd all the la...

Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes

In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-guided mining: given a metarule containing p predicates, we compare mining on an n-dimensional (n-D) cube structure (where p < n) with mining on smaller multiple pdimensional cubes. In addition, we propose an efficient m...

Modified Approach for Classifying Multi-Dimensional Data-Cube Through Association Rule Mining for Granting Loans in Bank

In this paper, modified Approach for classifying Multi-dimensional data cube is constructed. It explores data cubes in large Multi-Dimensional Schema. Numerical and Nominal attributes are categorized based on Principal Component Analysis. Semantic relationships are identified by applying Multidimensional scaling. Additionally, AR is integrated for finding the inserting measures. Many algorithms...

Using Data Cubes for Metarule-Guided Mining of Multi-Dimensional Association Rules

An Online Environment for Mining Association Rules in Multidimensional Data

Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniqu...