IceCube: efficient targeted mining in data cubes

We address the problem of mining targeted association rules over multidimensional market-basket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distributed over cells of an n-dimensional cube. In this framework, a targeted association rule is of the form {X -> Y} R, where R is a convex region in the cube and X. Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying targeted association rules. Then, we discuss the concepts of bottom-up aggregation and cubing, leading to the CellUnion technique. This approach is further extended, using notions of cube-count interleaving and credit-based pruning, to derive the IceCube algorithm. Our experiments demonstrate that IceCube consistently provides the best execution time performance, especially for large and complex data cubes

On control of nonlinear system dynamics at unstable steady state

Shifting an oscillatory or a chaotic trajectory to the unstable steady state of a nonlinear system in the presence of stochastic or deterministic load disturbances continues to be a nontrivial task. In the present work, two effective strategies for such control needs are presented. The control laws employed do not contain the process model parameters explicitly. The suggested strategies are demonstrated on two simulated nonlinear reaction systems exhibiting multi-stationarity, limit cycle oscillations, and chaos