12 research outputs found
Classifier PGN: Classification with High Confidence Rules
ACM Computing Classification System (1998): H.2.8, H.3.3.Associative classifiers use a set of class association rules, generated from a given training set, to classify new instances. Typically, these techniques set a minimal support to make a first selection of appropriate rules and discriminate subsequently between high and low quality rules by means of a quality measure such as confidence. As a result, the final set of class association rules have a support equal or greater than a predefined threshold, but many of them have confidence levels below 100%. PGN is a novel associative classifier which turns the traditional approach around and uses a confidence level of 100% as a first selection criterion, prior to maximizing the support. This article introduces PGN and evaluates the strength and limitations of PGN empirically. The results are promising and show that PGN is competitive with other well-known classifiers
Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery
Resource discovery is one of the key services in digitised cultural
heritage collections. It requires intelligent mining in heterogeneous digital
content as well as capabilities in large scale performance; this explains the
recent advances in classification methods. Associative classifiers are convenient
data mining tools used in the field of cultural heritage, by applying their
possibilities to taking into account the specific combinations of the attribute
values. Usually, the associative classifiers prioritize the support over the
confidence. The proposed classifier PGN questions this common approach and
focuses on confidence first by retaining only 100% confidence rules. The
classification tasks in the field of cultural heritage usually deal with data sets
with many class labels. This variety is caused by the richness of accumulated
culture during the centuries. Comparisons of classifier PGN with other
classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in
recognizing multi-class datasets on collections of masterpieces from different
West and East European Fine Art authors and movements
Mining Valuable Collaborations from Event Data Using the Recency-Frequency-Monetary Principle
Collaborative work leads to better organizational performance. However, a team leader’s view on collaboration does not always match reality. Due to the increased adoption of (online) collaboration systems in the wake of the COVID pandemic, more digital traces on collaboration are available for a wide variety of use cases. These traces allow for the discovery of accurate and objective insights into a team’s inner workings. Existing social network discovery algorithms however, are often not tailored to discover collaborations. These techniques often have a different view on collaboration by mostly focusing on handover of work, resource profile similarity, or establishing relationships between resources when they work on the same case or activities without any restrictions. Furthermore, only the frequency of appearance of patterns is typically used as a measure of interestingness, which limits the kind of insights one can discover. Therefore we propose an algorithm to discover collaborations from event data using a more realistic approach than basing collaboration on the sequence of resources that carry out activities for the same case. Furthermore, a new research path is explored by adopting the Recency-Frequency-Monetary (RFM) concept, which is used in the marketing research field to assess customer value, in this context to value both the resource and the collaboration on these three dimensions. Our approach and the benefits of adopting RFM to gain insights are empirically demonstrated on a use case of collaboratively developing a curriculum
The effect of family business professionalization as a multidimensional construct on firm performance
Emergency department crowding: Time to shift the paradigm from predicting and controlling to analysing and managing
status: publishe