In a world where electronic databases are used to store ever-increasing quantities of data it is becoming harder to mine useful information from them. Therefore there is a need for a highly scalable parallel architecture capable of handling the ever-increasing complexity of data mining problems. A cycle stealing network is one possible scalable solution to this problem. A cycle stealing network allows users to donate their idle cycles to form a virtual supercomputer by connecting multiple machines via a network. This research aims to establish whether cycle stealing networks, specifically the G2 system developed at the Queensland University of Technology, are viable for large scale data mining problems. The computationally intensive sequence mining, feature selection and functional dependency mining problems are deliberately chosen to test the usefulness and scalability of G2. Tests have shown that G2 is highly scalable where the ratio of computation to communication is approximately known. However for combinatorial problems where computation times are difficult or impossible to predict, and communication costs can be unpredictable, G2 often provides little or no speedup. This research demonstrates that existing sequence mining and functional dependency mining techniques are not suited to a client-server style cycle stealing network like G2. However the feature selection is well suited to G2, and a new sequence mining algorithm offers comparable performance to other existing, non-cycle stealing, parallel sequence mining algorithms. Furthermore new functional dependency mining algorithms offer substantial benefit over existing serial algorithms.