The exegesis of sacred rites in the Talmud is subject to a restriction on the iteration and composition of inference rules. In order to determine the scope and limits of that restriction, the sages of the Talmud deploy those very same inference rules. We present the remarkable features of this early use of self-reference to navigate logical constraints and uncover the hidden complexity behind t...

In this research a three staged hybrid genetic-fuzzy systems modeling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. The empirical data was collected in an earlier study in order to establish the relations among human capital, organizational support and innovativeness. The results demonstrate that the model based on the fuzzy ...

In this paper, we consider a generalization of the state-of-art discriminative method for optimizing the conditional likelihood in Hidden Markov Models (HMMs), called the Extended Baum-Welch (EBW) algorithm, that has had significant impact on the speech recognition community. We propose a generalized form of EBW update rules that can be associated with a weighted sum of updated and initial mode...

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good quality measure remains a challenging task for a user. Given a quality...

This paper presents the Croatian context-dependent acoustic modelling used in speech recognition and in speech synthesis. The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. For speech recognition and speech synthesis system modelling and testing the Croatian speech corpus VEPRAD was used. The experiments have shown that Croatian ...

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Di erent Interestingness Measures "IMs" were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good interestingness measure remains a challenging task for a ...

Mining hidden pattern from existing databases is an important topic in field of data mining. The knowledge obtained from these databases is used in different applications like in market basket analysis. Association Rules are important to discover the relationships among the attributes in a database. In general the rules generated by Association Rule Mining technique do not consider the negative...

We present a generalization of a strategy, called SCIKD, proposed in [7] that allows to reduce a disclosure risk of confidential data in an information system S [10] using methods based on knowledge discovery. The method proposed in [7] protects confidential data against Rule-based Chase, the null value imputation algorithm driven by certain rules [2], [4]. This method identifies a minimal subs...

The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and obse...

In this paper, we propose a new approach to evaluate the behavior of objective interestingness measures on association rules. The objective interestingness measures are ranked according to the most significant interestingness interval calculated from an inversely cumulative distribution. The sensitivity values are determined by this interval in observing the rules having the highest interesting...