A tutorial on statistically sound pattern discovery
W Hämäläinen, GI Webb - Data Mining and Knowledge Discovery, 2019 - Springer
Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …
overcome many of the issues that have hampered standard data mining approaches to …
A peek into the black box: exploring classifiers by randomization
Classifiers are often opaque and cannot easily be inspected to gain understanding of which
factors are of importance. We propose an efficient iterative algorithm to find the attributes …
factors are of importance. We propose an efficient iterative algorithm to find the attributes …
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
T De Bie - Data Mining and Knowledge Discovery, 2011 - Springer
Recent research has highlighted the practical benefits of subjective interestingness
measures, which quantify the novelty or unexpectedness of a pattern when contrasted with …
measures, which quantify the novelty or unexpectedness of a pattern when contrasted with …
An information theoretic framework for data mining
T De Bie - Proceedings of the 17th ACM SIGKDD international …, 2011 - dl.acm.org
We formalize the data mining process as a process of information exchange, defined by the
following key components. The data miner's state of mind is modeled as a probability …
following key components. The data miner's state of mind is modeled as a probability …
Tell me what i need to know: succinctly summarizing data with itemsets
Data analysis is an inherently iterative process. That is, what we know about the data greatly
determines our expectations, and hence, what result we would find the most interesting. With …
determines our expectations, and hence, what result we would find the most interesting. With …
Knowledge discovery interestingness measures based on unexpectedness
KN Kontonasios, E Spyropoulou… - … reviews: data mining …, 2012 - Wiley Online Library
Abstract Knowledge discovery methods often discover a large number of patterns. Although
this can be considered of interest, it certainly presents considerable challenges too. Indeed …
this can be considered of interest, it certainly presents considerable challenges too. Indeed …
A Unifying Framework for Mining Approximate Top- Binary Patterns
C Lucchese, S Orlando… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
A major mining task for binary matrixes is the extraction of approximate top-k patterns that
are able to concisely describe the input data. The top-k pattern discovery problem is …
are able to concisely describe the input data. The top-k pattern discovery problem is …
Subjective interestingness in exploratory data mining
T De Bie - International Symposium on Intelligent Data Analysis, 2013 - Springer
Exploratory data mining has as its aim to assist a user in improving their understanding
about the data. Considering this aim, it seems self-evident that in optimizing this process the …
about the data. Considering this aim, it seems self-evident that in optimizing this process the …
Summarizing data succinctly with the most informative itemsets
Knowledge discovery from data is an inherently iterative process. That is, what we know
about the data greatly determines our expectations, and therefore, what results we would …
about the data greatly determines our expectations, and therefore, what results we would …
The minimum description length principle for pattern mining: A survey
E Galbrun - Data mining and knowledge discovery, 2022 - Springer
Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …
the selection of patterns constitutes a major challenge. The Minimum Description Length …