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In this paper a new density-based, non-frequentistic data analytics tool, called typicality distribution function (TDF) is proposed. It is a further development of the recently introduced typicality- and eccentricity-based data analytics (TEDA) framework. The newly introduced TDF and its standardized form offer an effective alternative to the widely used probability distribution function (pdf), however,...
A recently introduced data density based approach to clustering, known as Data Density based Clustering has been presented which automatically determines the number of clusters. By using the Recursive Density Estimation for each point the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use. The Data Density based Clustering method however...
In this paper, we propose a new eccentricity- based anomaly detection principle and algorithm. It is based on a further development of the recently introduced data analytics framework (TEDA - from typicality and eccentricity data analytics). We compare TEDA with the traditional statistical approach and prove that TEDA is a generalization of it in regards to the well-known “nσ” analysis (TEDA gives...
In this paper a new method for definition of the antecedent/premise part of the fuzzy rule-based (FRB) systems is proposed. It removes the need to define the membership functions per variable using often artificial parametric functions such as triangular, Gaussian etc. Instead, it strictly follows the real data distribution and in this sense resembles particle filters. In addition, it is in a vector...
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