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- Robert Kessl, Nilothpal Talukder, Pranay Anchuri, Mohammed J. Zaki
- BigMine
- 2014

Discovering frequent subgraph patterns from a set of graphs (multi-graph) or a single graph. Practical applications in biology (PPI network), chemistry (similar compounds), or social networks (similar communities), etc The problem is computationally hard as it requires: Enumeration of an exponential number of candidate subgraph patterns Checking their… (More)

- Viliam Lisý, Robert Kessl, Tomás Pevný
- ECML/PKDD
- 2014

- Robert Kessl
- MLDM
- 2011

In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not… (More)

- Jan Curín, Pascal Fleury, Jan Kleindienst, Robert Kessl
- MLMI
- 2007

- Pascal Fleury, Jan Cuř́ın, Jan Kleindienst, Robert Kessl
- 2005

To make the task simpler to the user, context-aware applications try to infer the current context of the task through a set of sensors. These sensors deliver information which is the basis for the context model helping the application to take appropriate actions. Up to recently, most of the context-aware systems and frameworks made the assumption of dealing… (More)

- Robert Kessl
- IEEE Transactions on Knowledge and Data…
- 2016

Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent… (More)

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