Data Analysis and Algorithms

Research focused on algorithms and theory for data analysis and machine learning. It fundamentally explores areas related to clustering, classification, outlier detection, validity methods, data analysis methodology, big data and stream data scenarios.

CTC datasets for ML algorithm testing: covert timing channels

CTC datasets consist of a mix of preprocessed network traffic data with and without covert timing channels. They are a demanding challenge for machine learning and data mining algorithms.

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MDCGen: Generator of Multidimensional Datasets for Clustering

MDCGen is a tool for generating multidimensional synthetic datasets. It is devised for testing, evaluating and benchmarking clustering algorithms.

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ODTF: one-class decision tree fuzzifier

ODTF (One-class Decision Tree Fuzzyfier) is an algorithm that wraps a linear DT and establishes class-membership scores based on weighted distances to decision thresholds.

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GOI: indices for absolute cluster validation and dataset interpretation

GOI provides a set of indices for absolute cluster validation and for the interpretation of the dataset context based on geometrical properties of the multidimensional data.

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SDO: outlier detection based on low density models

SDO (Sparse Data Observers) is an algorithm that establihes distance-based outlierness scores on data samples. SDO is devised to be embedded in systems or frameworks that operate autonomously and must process large amounts of data in a continuos manner. SDO is a machine learning solution for Big Data and stream data applications.

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