Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 2

Volume 2, Number 1, January / February 2012

• Laura Elo, Benno Schwikowski:
  Mining proteomic data for biomedical research. 1-13
  
• Christopher Kauffman, George Karypis:
  Computational tools for protein-DNA interactions. 14-28
  

• Amit Banerjee, Rajesh N. Davé:
  Robust clustering. 29-59
  
• Guanling Lee, Yi-Chun Chen:
  Protecting Sensitive knowledge in association patterns mining. 60-68
  
• Dima Alberg, Mark Last, Abraham Kandel:
  Knowledge discovery in data streams with regression tree methods. 69-78
  
• Mohamed Medhat Gaber:
  Advances in data stream mining. 79-85
  

• Fionn Murtagh, Pedro Contreras:
  Algorithms for hierarchical clustering: an overview. 86-97
  

Volume 2, Number 2, March / April 2012

• Márcia D. B. Oliveira, João Gama:
  An overview of social network analysis. 99-115
  
• Xosé M. Fernández-Suarez:
  Analyzing genomic data: understanding the genome. 116-137
  

• Yann-Chang Huang, Chao-Ming Huang, Huo-Ching Sun:
  Data mining for oil-insulated power transformers: an advanced literature survey. 138-148
  
• Longbing Cao:
  Actionable knowledge discovery and delivery. 149-163
  

• J. P. Morgan, Xinwei Deng:
  Experimental design. 164-172
  
• Issam El-Naqa:
  Machine learning methods for predicting tumor response in lung cancer. 173-181
  
• Wil M. P. van der Aalst, Arya Adriansyah, Boudewijn F. van Dongen:
  Replaying history on process models for conformance checking and performance analysis. 182-192
  

Volume 2, Number 3, May / June 2012

• Frederic Stahl, Ivan Jordanov:
  An overview of the use of neural networks for data mining tasks. 193-208
  
• Yu Xiao, Jian Yu:
  Partitive clustering (K-means family). 209-225
  

• Georg Peters, Richard Weber:
  Dynamic clustering with soft computing. 226-236
  
• Peter Kokol, Sandi Pohorec, Gregor Stiglic, Vili Podgorelec:
  Evolutionary design of decision trees for medical application. 237-254
  

• Michael R. Chernick:
  Resampling methods. 255-262
  
• Marja A. Heiskanen, Tero Aittokallio:
  Mining high-throughput screens for cancer drug targets - lessons from yeast chemical-genomic profiling and synthetic lethality. 263-272
  
• Irina Mohorianu, Sara Lopez-Gomollon, Frank Schwach, Tamas Dalmay, Vincent Moulton:
  FiRePat - Finding Regulatory Patterns between sRNAs and Genes. 273-284
  
• Sanghamitra Bandyopadhyay:
  Genetic algorithms for clustering and fuzzy clustering. 285
  

Volume 2, Number 4, July / August 2012

• Alexander Fölling, Joachim Lepping:
  Knowledge discovery for scheduling in computational grids. 287-297
  
• Francisco G. Vital-Lopez, Vesna Memisevic, Bhaskar Dutta:
  Tutorial on biological networks. 298-325
  

• Lawrence O. Hall:
  Objective function-based clustering. 326-339
  
• Ira Assent:
  Clustering high dimensional data. 340-350
  

• Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
  Subspace clustering. 351-364
  

Volume 2, Number 5, September / October 2012

• Li-Zhi Liu, Fang-Xiang Wu, Wen-Jun Zhang:
  Reverse engineering of gene regulatory networks from biological data. 365-385
  

• Kleanthis-Nikolaos Kontonasios, Eirini Spyropoulou, Tijl De Bie:
  Knowledge discovery interestingness measures based on unexpectedness. 386-399
  
• Hiroshi Mamitsuka:
  Mining from protein-protein interactions. 400-410
  

• Martin Atzmueller:
  Mining social media: key players, sentiments, and communities. 411-419
  
• Cagatay Catal:
  Software mining and fault prediction. 420-426
  
• Kerri L. Clark, Stanley P. Kowalski:
  Harnessing the power of patent information to accelerate innovation. 427-435
  

Volume 2, Number 6, November / December 2012

• Christian Borgelt:
  Frequent item set mining. 437-456
  
• Shastri L. Nimmagadda, Heinz Dreher:
  On new emerging concepts of petroleum digital ecosystem. 457-475
  
• Aretha Barbosa Alencar, Maria Cristina Ferreira de Oliveira, Fernando Vieira Paulovich:
  Seeing beyond reading: a survey on visual text analytics. 476-492
  

• Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, Inke R. König:
  Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. 493-507
  

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