The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature...

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault...

Matrix decomposition methods: Singular Value Decomposition (SVD) and Semi Discrete Decomposition (SDD) are proved to be successful in dimensionality reduction. However, to the best of our knowledge, no empirical results are presented and no comparison between these methods is done to uncover...

Frequent pattern evaluation is imperative for cricket match data to develop more proficient coaching strategies and progress the performance of individual players. The rapid growth in size of the match database far exceeds the human ability to analyse, thus creating an opportunity to extract...

In this paper we propose two distinct ways of augmenting the existing clustering environment so that granular data (patterns) can be accommodated. The two approaches deal with either holistic or atomistic representations of metrics and descriptors when mining outcomes of granule-valued random...