Eirini Spyropoulou

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Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for highly simplified types of data, such as an attribute-value table or a binary database, such that those methods are not directly applicable to multi-relational data.(More)
Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for single-table databases, and are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more(More)
This paper suggests a framework for mining subjectively interesting pattern sets that is based on two components: (1) the encoding of prior information in a model for the data miner's state of mind; (2) the search for a pattern set that is maximally informative while efficient to convey to the data miner. We illustrate the framework with an instantiation(More)
Methods for local pattern mining are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on relational pattern mining has shown how the aforementioned pattern(More)
Web communities involve networks of loosely coupled data sources. Members in those communities should be able to pose queries and gather results from all data sources in the network, where available. At the same time, data sources should have limited restrictions on how to organize their data. If a global schema is not available for such a network, query(More)
Exploratory Data Mining (EDM), the contemporary heir of Exploratory Data Analysis (EDA) pioneered by Tukey in the seventies, is the task of facilitating the extraction of interesting nuggets of information from possibly large and complexly structured data. Major conceptual challenges in EDM research are the understanding of how one can formalise a nugget of(More)
The utility of a dense subgraph in gaining a better understanding of a graph has been formalised in numerous ways, each striking a different balance between approximating actual interestingness and computational efficiency. A difficulty in making this trade-off is that, while computational cost of an algorithm is relatively well-defined, a pattern’s(More)
Three recent trends aim to make local pattern mining more directly suited for use on data as it presents itself in practice, namely in a multi-relational form and affected by noise. The first of these trends is the generalisation of local pattern syntaxes to approximate, noise-tolerant, variants (notably fault-tolerant itemset mining and community(More)