Annalisa Appice
Annalisa Appice
Researcher of Computer Science, University of Bari Aldo Moro
Verified email at - Homepage
Cited by
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Geographic data mining and knowledge discovery
HJ Miller, J Han
CRC press, 2009
Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Preface
M Abe, K Aoki, G Ateniese, R Avanzi, Z Beerliovį, O Billet, A Biryukov, ...
Lecture Notes in Computer Science (including subseries Lecture Notes in …, 2006
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
A Appice, M Ceci, A Lanza, FA Lisi, D Malerba
Intelligent Data Analysis 7 (6), 541-566, 2003
Top-down induction of model trees with regression and splitting nodes
D Malerba, F Esposito, M Ceci, A Appice
IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5), 612-625, 2004
Mr-SBC: a multi-relational naive bayes classifier
M Ceci, A Appice, D Malerba
European conference on principles of data mining and knowledge discovery, 95-106, 2003
Mining spatial association rules in census data
D Malerba, F Esposito, FA Lisi, A Appice
Research in Official Statistics. v5 i1, 19-44, 2003
Stepwise induction of multi-target model trees
A Appice, S Džeroski
European conference on machine learning, 502-509, 2007
Redundant feature elimination for multi-class problems
A Appice, M Ceci, S Rawles, P Flach
Proceedings of the twenty-first international conference on Machine learning, 5, 2004
Empowering a GIS with inductive learning capabilities: the case of INGENS
D Malerba, F Esposito, A Lanza, FA Lisi, A Appice
Computers, Environment and Urban Systems 27 (3), 265-281, 2003
A co-training strategy for multiple view clustering in process mining
A Appice, D Malerba
IEEE transactions on services computing 9 (6), 832-845, 2015
Network regression with predictive clustering trees
D Stojanova, M Ceci, A Appice, S Džeroski
Data Mining and Knowledge Discovery 25 (2), 378-413, 2012
Spatial associative classification: propositional vs structural approach
M Ceci, A Appice
Journal of Intelligent Information Systems 27 (3), 191-213, 2006
Dealing with spatial autocorrelation when learning predictive clustering trees
D Stojanova, M Ceci, A Appice, D Malerba, S Džeroski
Ecological Informatics 13, 22-39, 2013
Mining and filtering multi-level spatial association rules with ARES
A Appice, M Berardi, M Ceci, D Malerba
International Symposium on Methodologies for Intelligent Systems, 342-353, 2005
Classification of symbolic objects: A lazy learning approach
A Appice, C d'Amato, F Esposito, D Malerba
Intelligent Data Analysis 10 (4), 301-324, 2006
Using multiple time series analysis for geosensor data forecasting
S Pravilovic, M Bilancia, A Appice, D Malerba
Information Sciences 380, 31-52, 2017
Mining spatial association rules in census data: a relational approach
D Malerba, FA Lisi, A Appice, F Sblendorio
Proceedings of the ECML/PKDD 2, 80-93, 2002
A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets
A Appice, M Ceci, A Turi, D Malerba
Intelligent data analysis 15 (1), 69-88, 2011
Summarizing numeric spatial data streams by trend cluster discovery
A Appice, A Ciampi, D Malerba
Data Mining and Knowledge Discovery 29 (1), 84-136, 2015
Dealing with temporal and spatial correlations to classify outliers in geophysical data streams
A Appice, P Guccione, D Malerba, A Ciampi
Information Sciences 285, 162-180, 2014
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