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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 4 Issue 3, October 2010

MARGIN: Maximal frequent subgraph mining
Lini T. Thomas, Satyanarayana R. Valluri, Kamalakar Karlapalem
Article No.: 10
DOI: 10.1145/1839490.1839491

The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs providing ample scope for pruning. MARGIN is a...

SCOAL: A framework for simultaneous co-clustering and learning from complex data
Meghana Deodhar, Joydeep Ghosh
Article No.: 11
DOI: 10.1145/1839490.1839492

For difficult classification or regression problems, practitioners often segment the data into relatively homogeneous groups and then build a predictive model for each group. This two-step procedure usually results in simpler, more interpretable...

BISC: A bitmap itemset support counting approach for efficient frequent itemset mining
Jinlin Chen, Keli Xiao
Article No.: 12
DOI: 10.1145/1839490.1839493

The performance of a depth-first frequent itemset (FI) miming algorithm is closely related to the total number of recursions. In previous approaches this is mainly decided by the total number of FIs, which results in poor performance when a large...

Efficient algorithms for large-scale local triangle counting
Luca Becchetti, Paolo Boldi, Carlos Castillo, Aristides Gionis
Article No.: 13
DOI: 10.1145/1839490.1839494

In this article, we study the problem of approximate local triangle counting in large graphs. Namely, given a large graph G=(V,E) we want to estimate as accurately as possible the number of triangles incident to every node...

Multilabel dimensionality reduction via dependence maximization
Yin Zhang, Zhi-Hua Zhou
Article No.: 14
DOI: 10.1145/1839490.1839495

Multilabel learning deals with data associated with multiple labels simultaneously. Like other data mining and machine learning tasks, multilabel learning also suffers from the curse of dimensionality. Dimensionality reduction has been...

Learning multiple nonredundant clusterings
Ying Cui, Xiaoli Z. Fern, Jennifer G. Dy
Article No.: 15
DOI: 10.1145/1839490.1839496

Real-world applications often involve complex data that can be interpreted in many different ways. When clustering such data, there may exist multiple groupings that are reasonable and interesting from different perspectives. This is especially...