Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

Mining Frequent Itemsets in Time-Varying Data Streams

Mining frequent itemsets in data streams is beneficial to many real-world applications but is also a challenging task since data streams are unbounded and have high arrival rates. Moreover, the distribution of data streams can change over time, which makes the task of maintaining frequent itemsets even harder. In this paper, we propose a falsenegative oriented algorithm, called TWIM, that can find most of the frequent itemsets, detect distribution changes, and update the mining results accordingly. Experimental results show that our algorithm performs as good as other false-negative algorithms on data streams without distribution change, and has the ability to detect changes over time-varying data streams in real-time with a high accuracy rate