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A contribution to the discovery of multidimensional patterns in healthcare trajectories

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2014年第2期42卷 283-305页

作者： Egho, Elias Jay, Nicolas Raissi, Chedy Ienco, Dino Poncelet, Pascal Teisseire, Maguelonne Napoli, Amedeo Univ Lorraine Inria Nancy Grand Est LORIA CNRS Nancy France Univ Montpellier 2 UMR TETIS LIRMM Irstea Montpellier France

sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing dimensional items. However, in real-world scenarios, data sequences are described as combination of both multidimensional items and itemsets. These heterogeneous descriptions cannot be handled by traditional approaches. In this paper we propose a new approach called MMISP (Mining multidimensional Itemset sequential patterns) to extract patterns from complex sequential database including both multidimensional items and itemsets. The novelties of the proposal lies in: (i) the way in which the data are efficiently compressed;(ii) the ability to reuse and adopt sequential pattern mining algorithms and (iii) the extraction of new kind of patterns. We introduce a case-study on real-world data from a regional healthcare system and we point out the usefulness of the extracted patterns. Additional experiments on synthetic data highlights the efficiency and scalability of the approach MMISP.

关键词： Complex sequential patterns multidimensional sequential patterns Data mining Complex data

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A scalable method of mining approximate multidimensional sequential patterns on distributed systemts

A scalable method of mining approximate multidimensional seq...

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6th International Conference on Machine Learning and Cybernetics

作者： Hu, Kong-Fa Zhang, Chang-Hai Chen, Ling Yangzhou Univ Dept Comp Sci & Engn Yangzhou 225009 Peoples R China

ISBN: (纸本)9781424409723

A scalable and effective algorithm called AMGMSP (Approximate Mining of Global multidimensional sequential patterns) is proposed to solve the problem of mining the multidimensional sequential patterns for large databases in the distributed environment. First, the multidimensional information is embedded into the corresponding sequences in order to convert the mining on the multidimensional sequential patterns to sequential patterns. Then the sequences are clustered, summarized, and analyzed on the distributed sites, and the local patterns could be obtained by the effective approximate sequential pattern mining method. Finally, the global multidimensional sequential patterns could be mined by high vote sequential patterns after collecting all the local patterns on one site. Both the theories and the experiments indicate that this method could simplify the problem of mining the multidimensional sequential patterns and avoid mining the redundant information. The global sequential patterns could be obtained effectively by the scalable method after reducing the cost of communication.

关键词： distributed database multidimensional sequential patterns approximate sequential pattern mining

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A SCALABLE METHOD OF MINING APPROXIMATE multidimensional sequential patterns ON DISTRIBUTED SYSTEMTS

A SCALABLE METHOD OF MINING APPROXIMATE MULTIDIMENSIONAL SEQ...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： KONG-FA HU CHANG-HAI ZHANG LING CHEN Department of Computer Science and Engineering Yangzhou University Yangzhou 225009China

A scalable and effective algorithm called AMGMSP (Approximate Mining of Global multidimensional sequential patterns) is proposed to solve the problem of mining the multidimensional sequential patterns for large databases in the distributed environment First, the multidimensional information is embedded into the corresponding sequences in order to convert the mining on the multidimensional sequential patterns to sequential *** the sequences are clustered, summarized, and analyzed on the distributed sites, and the local patterns could be obtained by the effective approximate sequential pattern mining ***, the global multidimensional sequential patterns could be mined by high vote sequential patterns after collecting all the local patterns on one *** the theories and the experiments indicate that this method could simplify the problem of mining the multidimensional sequential patterns and avoid mining the redundant *** global sequential patterns could be obtained effectively by the scalable method after reducing the cost of communication.

关键词： Distributed database multidimensional sequential patterns Approximate sequential pattern mining

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