In granular computing, each object is represented as an information granule and an information granule can be connected to other granules through semantic relationships. These connections can lead to a granular hierarchy or a network. Data mining of one set of objects may not be able to capture information contained in granular connections. This paper describes a concept of meta-clustering that clusters a set of granules using clustering information from another or the same set of networked granules. Cluster membership of one granule can affect another granule's cluster membership, resulting in a recursive meta-clustering process. We illustrate the usefulness of such meta-clustering for a granular hierarchy consisting of sets of businesses and reviewers, a set of networked granules representing mobile phone users, and trading patterns of financial instruments that are linked to each other through a temporal dimension. Keywords Meta-clustering Á Granular computing Á Iterative clustering Á k-means Á Fuzzy c-means Á Social networks Á Time series Á Financial markets Á Web mining

Literature reviews revealed that multicollinearity always exists when model a deals with several independent variables. This phenomenon can cause the t statistic and the related probability-value to give a misleading impression of the importance of the independent variables. There are two approaches in tackling this issue. The common approach is correlation-coefficient based and the other is variance-based. Many softwares in the market have highlighted this phenomenon and offer options in minimising the effect. Currently, the variance-based approach is widely available in the software market. This is because it does not depend on the type of dependent variables. This variance-based approach via Variance Inflation Factor (VIF) quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. Thus, here, a novel approach is revealed in detailing the procedures to remove several variables due to multicollinearity effects. Ultimately, the insignificant variables are eliminated. It is found that when a very stringent criterion is set for multicollinearity, the process of elimination of variables becomes smooth and easy besides shortening the number of iteration.

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