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Attenzione: questa notizia era valida fino al 12/06/2019. Attualmente è archiviata, quindi il suo contenuto potrebbe non essere più valido

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Seminar: Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

Who: prof. Alberto Fernández Oliva (Univ. Havana) 
When:  Tuesday June 11th - 11:30 AM
Where: Seminar room, Alan Turing building (2° floor) 
What: Seminar on "Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element.”

ABSTRACT

In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own
usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great
theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. In this seminar we present a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm.