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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Self-Organizing Maps
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PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using emerging pattern based projected clustering and gene expression data for cancer detection
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
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Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
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IEEE Transactions on Knowledge and Data Engineering
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ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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This paper describes an approach to identifying and comparing frequent pattern trends in social networks. A frequent pattern trend is defined as a sequence of time-stamped occurrence (support) values for specific frequent patterns that exist in the data. The trends are generated according to epochs. Therefore, trend changes across a sequence epochs can be identified. In many cases, a great many trends are identified and difficult to interpret the result. With a combination of constraints, placed on the frequent patterns, and clustering and cluster analysis techniques, it is argued that analysis of the result is enhanced. Clustering technique uses a Self Organising Map approach to produce a sequence of maps, one per epoch. These maps can then be compared and the movement of trends identified. This Frequent Pattern Trend Mining framework has been evaluated using two non-standard types of social networks, the cattle movement network and the insurance quote network.