Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...
We show how frequently occurring sequential patterns may be found from large datasets by first inducing a finite state automaton model describing the data, and then querying the m...
To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, ...
We introduce a novel framework (BLOSOM) for mining (frequent) boolean expressions over binary-valued datasets. We organize the space of boolean expressions into four categories: p...
Lizhuang Zhao, Mohammed J. Zaki, Naren Ramakrishna...
The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper, we provide a methodology, arch...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricar...