The original rough set theory deals with precise and complete data, while real applications frequently contain imperfect information. A typical imperfect data studied in rough set research is the missing values. Though there are many ideas proposed to solve the issue in the literature, the paper adopts a probabilistic approach, because it can incorporate other types of imperfect data including ...

Today, because of the rapid developments in both computer hardware and software industries, the increase in the storage capacities of huge databases, the data mining and the usage of the useful patterns that reside in the databases, became a very important research area. In parallel with the rapid increase in the data stored in the databases, effective use of the data is becoming a problem. To ...

Real-life data sets are often incomplete, i.e., some attribute values are missing. In this paper we compare traditional, frequently used methods of handling missing attribute values, which are based on preprocessing, with another class of methods dealing with missing attribute values in which rule induction is performed directly on incomplete data sets, i.e., handling missing attribute values a...

This paper suggests a method to develop rule-based models to systems with multiple inputs and outputs using concepts about rough sets. The rules provide relations among variables and give a mechanism to link granular descriptions of the models with the computational procedures used. An estimation procedure is applied to compute values from numeric representations encoded by rule sets. The metho...

The security provided by a firewall for a computer network almost completely depends on the rules it enforces. For over a decade, it has been a well-known and unsolved problem that the quality of many firewall rule sets is insufficient. Therefore, there are many tools to analyze them. However, we found that none of the available tools could handle typical, real-world iptables rulesets. This is ...

Building a classification system using datamining techniques has shown lower error rates than traditional algorithms such as decision trees. However, because the number of possible association rules in general is very large, algorithms are usually complicated and prone to overfitting. In this paper, we present a new algorithm that generates and uses a minimum set of association rules to form th...

Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection...

Data mining is the problem of inducing models from data. Models have both a descriptive and a predictive aspect. Descriptive models can be inspected and used for knowledge discovery. Models consisting of decision rules – such as those produced by methods from Pawlak’s rough set theory – are in principle descriptive, but in practice the induced models are too large to be inspected. In this thesi...

If-then rules are one of the most common forms of knowledge discovered by data mining methods. The number and the length of extracted rules tend to increase with the size of a database, making the rulesets less interpretable and useful. Existing methods of extracting fuzzy rules from numerical data improve the interpretability aspect, but the dimensionality of fuzzy rulesets remains high. In th...