ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Frequent sub-graph mining on edge weighted graphs
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
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The mining of social networks entails a high degree of computational complexity. This complexity is exacerbate when considering longitudinal social network data. To address this complexity issue three weighting schemes are proposed in this paper. The fundamental idea is to reduce the complexity by considering only the most significant nodes and links. The proposed weighting schemes have been incorporated into the weighted variations and extensions of the well established gSpan frequent subgraph mining algorithm. The focus of the work is the cattle movement network found in Great Britain. A complete evaluation of the proposed approaches is presented using this network. In addition, the utility of the discovered patterns is illustrated by constructing a sequential data set to which a sequential mining algorithm can be applied to capturing the changes in "behavior" represented by a network.