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2Learning Relational User Profiles and Recommending Items as Their Preferences Change
Z. Siddiqui, E. Tiakas, P. Symeonidis, M. Spiliopoulou, and Y. Manolopoulos. International Journal on Artificial Intelligence Tools 24 (02): 31 pages (2015)
9 years ago by @kmd-ovgu
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2Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis
T. Hielscher, M. Spiliopoulou, H. Völzke, and J. Kühn. Proc. of the 27th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS14) , Mount Sinai, NY, IEEE, (2014)
9 years ago by @kmd-ovgu
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1Subpopulation Discovery in Epidemiological Data with Subspace Clustering
U. Niemann, M. Spiliopoulou, H. Völzke, and J. Kühn. Foundations of Computing and Decision Sciences (FCDS) 39 (4): 271-300 (2014)
9 years ago by @kmd-ovgu
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3A Semi-supervised Incremental Clustering Algorithm for Streaming Data
M. Halkidi, M. Spiliopoulou, and A. Pavlou. Proc. of 16th Pacific-Asian Conf. on Knowledge Discovery (PAKDD 2012) , Kuala Lumpur, Malaysia, (May 2012)
9 years ago by @kmd-ovgu
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1Advanced Topics on Data Stream Mining: Part II. Mining Multiple Streams
J. Gama, M. Spiliopoulou, and G. Krempl. Tutorial at the 23rd Europ. Conf. on Machine Learning and16th Europ. Conf. on Principles and Practice ofKnowledge Discovery in Databases (ECML PKDD\'12), (24-28 Sept. 2012)
9 years ago by @kmd-ovgu
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1Discovering and Monitoring Product Features and the Opinions on them with OPINSTREAM
M. Zimmermann, E. Ntoutsi, and M. Spiliopoulou. Neurocomput. (2014)accepted 4/2014, to appear 2014.
9 years ago by @kmd-ovgu
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3Adaptive Semi Supervised Opinion Classifier With Forgetting Mechanism
M. Zimmermann, E. Ntoutsi, and M. Spiliopoulou. Proc. of the 29th Annual ACM Symposium on Applied Computing , ACM, (2014)
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4Online Clustering of High-Dimensional Trajectories under Concept Drift
G. Krempl, Z. Siddiqui, and M. Spiliopoulou. Machine Learning and Knowledge Discovery in Databases , volume 6912 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2011)
9 years ago by @kmd-ovgu
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1A data generator for multi-stream data
Z. Siddiqui, M. Spiliopoulou, S. Panagiotis, and T. Eleftherios. The second International Workshop on Mining Ubiquitous and Social Environments, MUSE '11, September 5, 2011 Athens, Greece , (2011)
9 years ago by @kmd-ovgu
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3Classification Rule Mining for a Stream of Perennial Objects.
Z. Siddiqui, and M. Spiliopoulou. RuleML Europe , volume 6826 of Lecture Notes in Computer Science, page 281-296. Springer, (2011)
9 years ago by @kmd-ovgu
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4Discovering Global and Local Bursts in a Stream of News
M. Zimmermann, I. Ntoutsi, Z. Siddiqui, M. Spiliopoulou, and H. Kriegel. Proceedings of the 27th Annual ACM Symposium on Applied Computing , page 807--812. New York, NY, USA, ACM, (2012)
9 years ago by @kmd-ovgu
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3Where Are We Going? Predicting the Evolution of Individuals
Z. Siddiqui, M. Oliveira, J. Gama, and M. Spiliopoulou. Advances in Intelligent Data Analysis XI , volume 7619 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2012)
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3Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?
S. Glaßer, U. Niemann, B. Preim, and M. Spiliopoulou. Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on , page 77-82. (June 2013)
9 years ago by @kmd-ovgu
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3Extracting Opinionated (Sub)Features from a Stream of Product Reviews
M. Zimmermann, E. Ntoutsi, and M. Spiliopoulou. Discovery Science , volume 8140 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
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3Framework for Storing and Processing Relational Entities in Stream Mining
P. Matuszyk, and M. Spiliopoulou. Advances in Knowledge Discovery and Data Mining , volume 7819 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
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3Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region
S. Glaßer, U. Niemann, U. Preim, B. Preim, and M. Spiliopoulou. Bildverarbeitung für die Medizin 2013 , Springer Berlin Heidelberg, (2013)
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2Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis
U. Niemann, H. Völzke, J. Kühn, and M. Spiliopoulou. Expert Systems with Applications 41 (11): 5405-5415 (September 2014)ESWA IMPACT FACTOR: 1.965 (2013).
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2Learning Relational User Profiles and Recommending Items as Their Preferences Change

2Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis

1Subpopulation Discovery in Epidemiological Data with Subspace Clustering

3A Semi-supervised Incremental Clustering Algorithm for Streaming Data

1Advanced Topics on Data Stream Mining: Part II. Mining Multiple Streams

1Discovering and Monitoring Product Features and the Opinions on them with OPINSTREAM

3Adaptive Semi Supervised Opinion Classifier With Forgetting Mechanism

4Online Clustering of High-Dimensional Trajectories under Concept Drift

1A data generator for multi-stream data

3Classification Rule Mining for a Stream of Perennial Objects.

4Discovering Global and Local Bursts in a Stream of News

3Where Are We Going? Predicting the Evolution of Individuals

3Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?

3Extracting Opinionated (Sub)Features from a Stream of Product Reviews

3Framework for Storing and Processing Relational Entities in Stream Mining

3Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region

2Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis

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