Sequential pattern mining has been an emerging problem in data mining. In this paper, we propose a new algorithm for mining frequent sequences. It processes only one scan of the da...
We present a closed set data mining paradigm which is particularly e ective for uncovering the kind of deterministic, causal dependencies that characterize much of basic science. ...
Several algorithms have been proposed for finding the “best,” “optimal,” or “most interesting” rule(s) in a database according to a variety of metrics including confid...
We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separatel...
In this paper, we propose a new mining task: mining top-k frequent closed patterns of length no less than min , where k is the desired number of frequent closed patterns to be min...