Begum, Nurjahan

Given the ubiquity of time series data in scientific, medical and financial domains, data miners have made substantial efforts to design efficient algorithms for classification, clustering, rule discovery and anomaly detection for this data type. In particular, all these well-known problems are important as exploratory techniques, and as sub-module...

Jalali, Laleh

With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monot...

Kuznetsov, Sergei O. Napoli, Amedeo Rudolph, Sebastian

These are the proceedings of the fifth edition of the FCA4AI workshop (http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the FCA4AI workshop is to investiga...

Jalali, Laleh

With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monot...

Lejeune, Gaël Rioult, François Cremilleux, Bruno

Conversational agents are more and more investigated by the community but their ability to keep the user committed in the interaction is limited. Predicting the behavior of children in a human-machine interaction setting is a key issue for the success of narrative conversational agents. In this paper, we investigate solutions to evaluate the child'...

Begum, Nurjahan

Given the ubiquity of time series data in scientific, medical and financial domains, data miners have made substantial efforts to design efficient algorithms for classification, clustering, rule discovery and anomaly detection for this data type. In particular, all these well-known problems are important as exploratory techniques, and as sub-module...

Nalenz, Malte

This work proposes Hs-RuleFit, a learning method for regression and classiﬁcation, which combines rule ensemble learning based on the RuleFit algorithm with Bayesian regularization through the horseshoe prior. To this end theoretical properties and potential problems of this combination are studied. A second step is the implementation, which utiliz...

Jalali, Laleh

With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monot...

Jalali, Laleh

With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monot...

Begum, Nurjahan

Given the ubiquity of time series data in scientific, medical and financial domains, data miners have made substantial efforts to design efficient algorithms for classification, clustering, rule discovery and anomaly detection for this data type. In particular, all these well-known problems are important as exploratory techniques, and as sub-module...