Anónimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing [...]

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May 8 2012

Fractal-Based Intrinsic Dimension Estimation and It ...

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Dimensionality reduction is an important step in knowledge discovery in databases. Intrinsic dimension indicates the number of variables necessary to describe a data set. Two methods, box-counting dimension and correlation dimension, are commonly used for intrinsic dimension estimation. However, the robustness of these two methods has not been rigorously studied. This paper demonstrates that correlation [...]

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May 3 2012

Extending Attribute Information for Small Data Set ...

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Data quantity is the main issue in the small data set problem, because usually insufficient data will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. This paper proposes a new attribute construction approach which converts the original data attributes into [...]

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Apr 28 2012

Discover Dependencies from Data—A Review

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Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies [...]

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May 5 2012

Efficiently Indexing Large Sparse Graphs for Simila ...

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The graph structure is a very important means to model schemaless data with complicated structures, such as protein-protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks. This paper focuses on the index structure for similarity search on a set of large sparse graphs and proposes an efficient indexing mechanism by introducing the [...]

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Apr 27 2012

DDD: A New Ensemble Approach for Dealing with Conce ...

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Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different [...]

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