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16. PAKDD Workshops 2012: Kuala Lumpur, Malaysia
- Takashi Washio, Jun Luo:
Emerging Trends in Knowledge Discovery and Data Mining - PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust, and DSDM, Kuala Lumpur, Malaysia, May 29 - June 1, 2012, Revised Selected Papers. Lecture Notes in Computer Science 7769, Springer 2013, ISBN 978-3-642-36777-9 - Viktor Gál, Illés Solt, Etienne E. Kerre, Mike Nachtegael:
Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors. 1-10 - Didier Breton, Sandra Bringay, François Marques, Pascal Poncelet, Mathieu Roche:
Mining Web Data for Epidemiological Surveillance. 11-21 - Jochen De Weerdt, Filip Caron, Jan Vanthienen, Bart Baesens:
Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining. 22-35 - Reid A. Johnson, Yang Yang, Everaldo Aguiar, Andrew K. Rider, Nitesh V. Chawla:
ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain. 36-46 - Chunju Zhang, Xueying Zhang, Chaoli Du:
The Relevance of Spatial Relation Terms and Geographical Feature Types. 47-56 - José M. Perea-Ortega, Miguel Ángel García Cumbreras, Luis Alfonso Ureña López:
Applying NLP Techniques for Query Reformulation to Information Retrieval with Geographical References. 57-69 - João M. M. Duarte, Ana L. N. Fred, F. Jorge F. Duarte:
Adaptive Evidence Accumulation Clustering Using the Confidence of the Objects' Assignments. 70-87 - Yong Liu, Shizhong Liao:
An Explicit Description of the Extended Gaussian Kernel. 88-99 - Hongwu Qin, Xiuqin Ma, Tutut Herawan, Jasni Mohamad Zain:
An Improved Genetic Clustering Algorithm for Categorical Data. 100-111 - Xin Xia, Xiaohu Yang, Shanping Li, Chao Wu:
Instance-Ranking: A New Perspective to Consider the Instance Dependency for Classification. 112-123 - Aina Musdholifah, Siti Zaiton Mohd Hashim:
Triangular Kernel Nearest-Neighbor-Based Clustering Algorithm for Discovering True Clusters. 124-140 - Nurul Fariza Zulkurnain, David J. Haglin, John A. Keane:
DisClose: Discovering Colossal Closed Itemsets via a Memory Efficient Compact Row-Tree. 141-156
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