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ACM Transactions on Knowledge Discovery from Data
ACM Press
  1-10 of 26 reviews Date Reviewed 
  Structural analysis of user choices for mobile app recommendation
Liu B., Wu Y., Gong N., Wu J., Xiong H., Ester M.  ACM Transactions on Knowledge Discovery from Data 11(2): 1-23, 2016. Type: Article

Mobile apps are rapidly evolving due to ongoing improvements in smartphone technology. However, the use of mobile devices introduces some obstacles for rookie users. How should novice users locate suitable apps from a hierarchy of apps to accompli...

Mar 14 2017
  Co-clustering structural temporal data with applications to semiconductor manufacturing
Zhu Y., He J.  ACM Transactions on Knowledge Discovery from Data 10(4): 1-18, 2016. Type: Article

New improvements in storage, measurement, and control methods in semiconductor engineering are rapidly producing more data. Today, there are valuable tools for monitoring and gathering time-based data for manufacturing devices such as integrated c...

Nov 15 2016
   Catching synchronized behaviors in large networks: a graph mining approach
Jiang M., Cui P., Beutel A., Faloutsos C., Yang S.  ACM Transactions on Knowledge Discovery from Data 10(4): 1-27, 2016. Type: Article

The automatic detection and accurate interpretation of suspicious graph patterns is one of the key issues in spotting malicious activities inside real-world systems, such as fake followers in Twitter, social network manipulation, and distributed d...

Nov 10 2016
  Kernelized information-theoretic metric learning for cancer diagnosis using high-dimensional molecular profiling data
Xiong F., Kam M., Hrebien L., Wang B., Qi Y.  ACM Transactions on Knowledge Discovery from Data 10(4): Article No. 38, 2016. Type: Article

The molecular gene expressions of tumor and blood samples are useful for detecting cancers. But the design of algorithms for diagnosing cancer using high-dimensional heterogeneous signatures of gene expression data is difficult. How should the hig...

Sep 9 2016
  Refining social graph connectivity via shortcut edge addition
Papagelis M.  ACM Transactions on Knowledge Discovery from Data 10(2): 1-35, 2015. Type: Article

Small changes to the structure of graphs imply a huge impact on their connectivity. While in a purely theoretical approach, a topological change affects just formal properties, graph data structures associated with well-defined semantics (for exam...

Dec 14 2015
   Improving top-N recommendation for cold-start users via cross-domain information
Mirbakhsh N., Ling C.  ACM Transactions on Knowledge Discovery from Data 9(4): 1-19, 2015. Type: Article

Collaborative recommender systems often provide disappointing suggestions to new users who volunteered very few or no ratings of their own for processing: this is known as the cold-start problem. Mitigating such shortcomings with cross-domain info...

Sep 29 2015
  Selecting the right correlation measure for binary data
Duan L., Street W., Liu Y., Xu S., Wu B.  ACM Transactions on Knowledge Discovery from Data 9(2): 1-28, 2014. Type: Article

Intelligent data mining algorithms call for reliable indicators of relationships in massive datasets. How should correlations be selected for the precise analysis of binary data from different problem areas? Duan et al. critique the strengths and ...

May 1 2015
  User vulnerability and its reduction on a social networking site
Gundecha P., Barbier G., Tang J., Liu H.  ACM Transactions on Knowledge Discovery from Data 9(2): 1-25, 2014. Type: Article

Social media users sometimes expose their friends to confidentiality and safety breaches. To what extent are companionable users vulnerable to security breaches in social networking sites? How should access to social networks and security risks be...

Dec 8 2014
  Discovering social circles in ego networks
McAuley J., Leskovec J.  ACM Transactions on Knowledge Discovery from Data 8(1): 1-28, 2014. Type: Article

A social circle in a user’s ego network is a group of interconnected people that have common attributes between themselves and the user. In order to automatically detect such circles, an unsupervised/semi-supervised learning approach is desi...

Oct 9 2014
  A regularization approach to learning task relationships in multitask learning
Zhang Y., Yeung D.  ACM Transactions on Knowledge Discovery from Data 8(3): 1-31, 2013. Type: Article

Multitask learning is very popular in many domains and has been applied in numerous applications. This paper proposes a novel regularization approach, multitask relationship learning (MTRL), to learn task relationships in multitask learning. One b...

Jul 1 2014
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