An information-theoretic framework for process structure and data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4081 LNCS, Issue , 2006, Pages 248-259

  Chiaravalloti, Antonio D ^b   Greco, Gianluigi ^a   Guzzo, Antonella ^b   Pontieri, Luigi ^b  

^a UNIVERSITY OF CALABRIA   (Italy)

^b CNR   (Italy)

Author keywords

Coclustering; Mutual information; Process mining; Workflow management

Indexed keywords

CORRELATION METHODS; INFORMATION ANALYSIS; INFORMATION MANAGEMENT; MATHEMATICAL MODELS; PROCESS CONTROL; REAL TIME SYSTEMS;

COCLUSTERING; MUTUAL INFORMATION; PROCESS MINING; WORKFLOW MANAGEMENT;

DATA MINING;

EID: 33751366118     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11823728_24     Document Type: Conference Paper

References (12)

1. I. S. Dhillon. Co-clustering documents and words using bipartite spectral graph partitioning. In Proc. Intl. Conf. on Knowledge Discovery and Data Mining (KDD01), pp. 269-274, 2001.
2. P. Berkhin and J. D. Becher. Learning simple relations: Theory and applications. In Proc. SIAM Intl. Conf. on Data Mining (SDM02), 2002.
3. I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In Proc. Intl. Conf. on Knowledge Discovery and Data Mining (KDD03), pp. 89-98, 2003.
4. B. Gao, T.-Y. Liu, X. Zheng, Q.-S. Cheng, and W.-Y. Ma. Consistent bipartite graph copartitioning for star-structured high-order heterogeneous data co-clustering. In Proc. Intl. Conf. on Knowledge Discovery and Data mining (KDD05)), pp. 41-50, 2005.
5. G. Greco, A. Guzzo, L. Pontieri, and D. Saccà. Mining expressive process models by clustering workflow traces. In Proc. Pacific-Asia Conference (PAKDD'04), pp. 52-62, 2004.
6. J. Herbst and D. Karagiannis. Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. Journal of Intelligent Systems in Accounting, Finance and Management, 9:67-92, 2000.
7. S. Hwang and W. Yang. On the discovery of process models from their instances. Decision Support Systems, 34(1):41-57, 2002.
8. G. Schimm. Mining most specific workflow models from event-based data. In Proc. Intl. Conf. on Business Process Management, pp. 25-40, 2003.
9. W. van der Aalst, A. Weijters, and L. Maruster. Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(9): 1128-1142, 2004.
10. B. van Dongen and W. van der Aalst. Multi-phase process mining: Aggregating instance graphs into EPCs and Petri nets. In Proc. Intl. Work, on Applications of Petri Nets to Coordination, Worklflow and Business Process Management (PNCWB) at the ICATPN'05, 2005.
11. S. Madeira and A. Oliveira. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1:24-45.
12. H. Zha, X. He, C. Ding, H. Simon, and M. Gu. Bipartite graph partitioning and data clustering. In Proc. Intl. Conf. on Information and Knowledge Management (CIKM'01), pp. 25-32, 2001.