Vestigios de latinidad clásica en reglas monásticas visigodas

Las reglas monásticas visigodas constituyen un corpus documental de importancia fundamental para el estudio del monacato primitivo en Hispania. Ellas reflejan, entre otros aspectos, la preocupación por los ideales de vida de su época, heredados en parte de diversos tratados clásicos, que expresaban, desde aspectos más profundos vinculados a la moral, hasta reglas más precisas relacionadas con el modo de comportamiento en sociedad. En la temprana Edad Media, esta preocupación fue retomada por las comunidades monásticas, rescatando elementos propios de las costumbres latinas tradicionales. En dicho contexto, este estudio se propone analizar la presencia de vestigios de latinidad clásica en las reglas monacales visigodas, las cuales sirvieron como receptáculo de aquellas tradiciones destinadas a regular los distintos aspectos de la vida del monje.

An Arabic Treatise on the Rules of the Maronite Monks

El propósito de este artículo es editar y republicar el texto de una interesante pieza que trata de las reglas de los monjes maronitas, así como ofrecer una análisis de sus contenidos, sus rasgos lingüísticos y el valor del mismo.

[Fragmenta varia graeca]

Collection of Greek texts from 14 distinct manuscripts; 15th century and later.

The ancren riwle; a treatise on the rules and duties of monastic life.

Text is that of the Cottonian manuscript (Nero A. XIV.) in the British Museum, collated with Titus p. XVIII. and Cleopatra C. VI.

A Reliable Basis for Approximate Association Rules

For most of the work done in developing association rule mining, the primary focus has been on the efficiency of the approach and to a lesser extent the quality of the derived rules has been emphasized. Often for a dataset, a huge number of rules can be derived, but many of them can be redundant to other rules and thus are useless in practice. The extremely large number of rules makes it difficult for the end users to comprehend and therefore effectively use the discovered rules and thus significantly reduces the effectiveness of rule mining algorithms. If the extracted knowledge can’t be effectively used in solving real world problems, the effort of extracting the knowledge is worth little. This is a serious problem but not yet solved satisfactorily. In this paper, we propose a concise representation called Reliable Approximate basis for representing non-redundant approximate association rules. We prove that the redundancy elimination based on the proposed basis does not reduce the belief to the extracted rules. We also prove that all approximate association rules can be deduced from the Reliable Approximate basis. Therefore the basis is a lossless representation of approximate association rules.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Concise representations for association rules in multi-level datasets

Association rule mining has made many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called Reliable basis for representing non-redundant association rules for both exact rules and approximate rules. An important contribution of this paper is that we propose to use the certainty factor as the criteria to measure the strength of the discovered association rules. With the criteria, we can determine the boundary between redundancy and non-redundancy to ensure eliminating as many redundant rules as possible without reducing the inference capacity of and the belief to the remaining extracted non-redundant rules. We prove that the redundancy elimination based on the proposed Reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the Reliable basis. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules.