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Most of the existing discretization methods such as k-interval discretization, equal width and equal frequency methods do not take the dependencies of decision attributes on condition attributes into account. In this paper, we propose a discretization algorithm that can keep the dependencies of the decision attribute on condition attributes, or keep the positive regions of the partition of the decision attribute. In the course of inducing classification rules from a data set, keeping these dependencies can achieve getting the set of the least condition attributes and the highest classification precision. [ABSTRACT FROM AUTHOR]

In a real world data set there are usually four kinds of mistaken values, the first one is the mistake in unit; the second one is the mistake of putting the radix points in wrong place, the third one is a scribal error, and the fourth one is a computational mistake. In this paper, we propose two algorithms for finding these four kinds of mistaken data. SARS and coronary heart disease data sets experimental results show that the two algorithms are available, that is, using the two algorithms we find some mistakes in the SARS and coronary heart disease data sets, and the results correspond to that found manually by medical experts. [ABSTRACT FROM AUTHOR]

In fault diagnosis and medical diagnosis fields, often there is more than one fault or disease that occur together. In order to obtain the factors that cause a single fault to change to multi-faults, the standard rough set based methods should be rebuilt. In this paper, we propose a decernibilty matrix based algorithm with which the cause of every single fault to change to multi-faults can be revealed. Additionally, we propose another rough set based algorithm to induce the common causes of all the single faults to change to their corresponding multi-faults, which is a process of knowledge discovery in rule base, i.e., not the usual database. Inducing more abstract rules in knowledge base is a very challenging problem that has not been resolved well. [ABSTRACT FROM AUTHOR]

Rough set based rule generation methods need discretization of the continuous values. However, most existing discretization methods cause inconsistencies. In this paper, we propose an algorithm that can eliminate the inconsistencies caused during the course of discretization. The algorithm can be integrated into the discaretization algorithms that cannot avoid causing inconsistencies to eliminate the inconsistencies. Three data experimental results show that the algorithm is available. [ABSTRACT FROM AUTHOR]