@inproceedings{orlando02efficient, title = {An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets}, author = {Salvatore Orlando and Paolo Palmerini and Raffaele Perego and Fabrizio Silvestri}, booktitle = {High Performance Computing for Computational Science — VECPAR 2002}, pages = {3--29}, year = 2003, url = {http://dx.doi.org/10.1007/3-540-36569-9_28}, description = {SpringerLink - Book Chapter}, abstract = {Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.}, biburl = {http://www.bibsonomy.org/bibtex/2522c68b8bb5e28f1bf9f1e11e612f542/jaeschke}, keywords = {set algorithm fca frequent parallel mining itemset} } @article{tkde06, title = {Fast and Memory Efficient Mining of Frequent Closed Itemsets}, author = {Claudio Lucchese and Salvatore Orlando and Raffaele Perego}, journal = {IEEE Transactions On Knowledge and Data Engineering}, number = 1, pages = {21--36}, volume = 18, year = 2006, biburl = {http://www.bibsonomy.org/bibtex/23aff1098bf9828a0c6683f07145d60bb/jaeschke}, keywords = {rule closed itemset mining frequent fca association} } @inproceedings{jaeschke2007analysis, title = {Analysis of the Publication Sharing Behaviour in {BibSonomy}}, address = {Berlin, Heidelberg}, author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Gerd Stumme}, booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)}, editor = {U. Priss and S. Polovina and R. Hill}, month = {July}, pages = {283--295}, publisher = {Springer-Verlag}, series = {Lecture Notes in Artificial Intelligence}, volume = 4604, year = 2007, isbn = {3-540-73680-8}, vgwort = {22}, abstract = {BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.}, biburl = {http://www.bibsonomy.org/bibtex/20c2b212b9ea3d822bf4729fd5fe6b6e1/jaeschke}, keywords = {bibsonomy iccs myown analysis l3s social 2007 trias folksonomy bookmarking fca} } @inproceedings{DBLP:conf/iccs/GanterR01, title = {Formal Concept Analysis Methods for Dynamic Conceptual Graphs.}, author = {Bernhard Ganter and Sebastian Rudolph}, booktitle = {Proceedings of the 9th International Conference on Conceptual Structures (ICCS 2001)}, crossref = {DBLP:conf/iccs/2001}, editor = {Harry S. Delugach and Gerd Stumme}, pages = {143-156}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 2120, year = 2001, ee = {http://link.springer.de/link/service/series/0558/bibs/2120/21200143.htm}, bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {3-540-42344-3}, biburl = {http://www.bibsonomy.org/bibtex/2a25ab4987c25f31c9c7a69b9925ec8f9/jaeschke}, keywords = {fca graphs dynamic lattice formal concept analysis} } @inproceedings{troy2007faster, title = {Faster Concept Analysis}, address = {Berlin, Heidelberg}, author = {Adam D. Troy and Guo-Qiang Zhang and Ye Tian}, booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)}, editor = {U. Priss and S. Polovina and R. Hill}, pages = {206--219}, publisher = {Springer-Verlag}, series = {Lecture Notes in Artificial Intelligence}, volume = 4604, year = 2007, url = {http://newton.cwru.edu/papers/MCA.pdf}, abstract = {We introduce a simple but efficient, multistage algorithm for constructing concept lattices (MCA). A concept lattice can be obtained as the closure system generated from attribute concepts (dually, object concepts). There are two strategies to use this as the basis of an algorithm: (a) forming intersections by joining one attribute concept at a time, and (b) repeatedly forming pairwise intersections starting from the attribute concepts. A straightforward translation of (b) to an algorithm suggests that pairwise intersection be performed among all cumulated concepts. MCA is parsimonious in forming the pairwise intersections: it only performs such operations among the newly formed concepts from the previous stage, instead of cumulatively. We show that this parsimonious multistage strategy is complete: it generates all concepts. To make this strategy really work, one must overcome the need to eliminate duplicates (and potentially save time even further), since concepts generated at a later stage may have already appeared in one of the earlier stages. As considered in several other algorithms in the literature [5], we achieve this by an auxiliary search tree which keeps all existing concepts as paths from the root to a flagged node or a leaf. The depth of the search tree is bounded by the total number of attributes, and hence the time complexity for concept lookup is bounded by the logarithm of the total number of concepts. For constructing lattice diagrams, we adapt a sub-quadratic algorithm of Pritchard [9] for computing subset partial orders to constructing the Hasse diagrams. Instead of the standard expected quadratic complexity, the Pritchard approach achieves a worst-case time O(N2/log N). Our experimental results showed significant improvements in speed for a variety of input profiles against three leading algorithms considered in the comprehensive comparative study [5]: Bordat, Chein, and Norris.}, biburl = {http://www.bibsonomy.org/bibtex/294f0a8c1cb1719df010e74056193e6b4/jaeschke}, keywords = {algorithm formal fast concept fca analysis} } @inproceedings{jaeschke2006trias, title = {TRIAS - An Algorithm for Mining Iceberg Tri-Lattices}, address = {Hong Kong}, author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme}, booktitle = {Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)}, month = {December}, pages = {907-911}, publisher = {IEEE Computer Society}, year = 2006, url = {http://www.kde.cs.uni-kassel.de/jaeschke/paper/jaeschke06trias.pdf}, issn = {1550-4786}, isbn = {0-7695-2701-9}, vgwort = {19}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162}, biburl = {http://www.bibsonomy.org/bibtex/2e387c294129e11f4221514d5fa807e26/jaeschke}, keywords = {trias trias_example 12 l3s iccs_example 2006 algorithm myown fca} } @inproceedings{1244292, title = {FCA-based approach for mining contextualized folksonomy}, address = {New York, NY, USA}, author = {Hak Lae Kim and Suk Hyung Hwang and Hong Gee Kim}, booktitle = {SAC '07: Proceedings of the 2007 ACM symposium on Applied computing}, pages = {1340--1345}, publisher = {ACM Press}, year = 2007, url = {http://portal.acm.org/citation.cfm?id=1244002.1244292&coll=GUIDE&dl=}, location = {Seoul, Korea}, isbn = {1-59593-480-4}, doi = {http://doi.acm.org/10.1145/1244002.1244292}, biburl = {http://www.bibsonomy.org/bibtex/24440c3ca148004f3759456eac34e84fa/jaeschke}, keywords = {social folksonomy formal concept fca analysis tagging network mining} } @inproceedings{hotho03explaining, title = {Explaining Text Clustering Results using Semantic Structures}, address = {Heidelberg}, author = {Andreas Hotho and Steffen Staab and Gerd Stumme}, booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases}, editor = {Nada Lavra\v{c} and Dragan Gamberger and Hendrik BlockeelLjupco Todorovski}, pages = {217-228}, publisher = {Springer}, series = {LNAI}, volume = 2838, year = 2003, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf}, comment = {alpha}, abstract = {Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.}, biburl = {http://www.bibsonomy.org/bibtex/253a943b6be4b34cf4e5329d0b58e99f6/jaeschke}, keywords = {trias_example concept clustering iccs_example text formal fca ontology} } @inproceedings{hotho02conceptualclustering, title = {Conceptual Clustering of Text Clusters}, author = {A. Hotho and G. Stumme}, booktitle = {Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)}, editor = {G. K\'okai and J. Zeidler}, pages = {37-45}, year = 2002, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/FGML02.pdf}, comment = {alpha}, biburl = {http://www.bibsonomy.org/bibtex/2e253c44552a046fe90236274bcfeab13/jaeschke}, keywords = {formal fca clustering concept analysis trias_example iccs_example text} } @inproceedings{stumme05finite, title = {A Finite State Model for On-Line Analytical Processing in Triadic Contexts.}, author = {Gerd Stumme}, booktitle = {Proceedings of the 3rd International Conference on Formal Concept Analysis}, editor = {Bernhard Ganter and Robert Godin}, pages = {315-328}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 3403, year = 2005, ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}, isbn = {3-540-24525-1}, biburl = {http://www.bibsonomy.org/bibtex/2840d97c6873133e49d39b1207f762430/jaeschke}, keywords = {fca trias_example iccs_example context triadic olap} } @inproceedings{lw95triadic, title = {A triadic approach to formal concept analysis}, author = {F. Lehmann and R. Wille}, booktitle = {Conceptual structures: applications, implementation and theory}, editor = {G. Ellis and R. Levinson and W. Rich and J. F. Sowa}, pages = {32-43}, publisher = {Springer Verlag}, series = {Lecture Notes in Artificial Intelligence}, volume = 954, year = 1995, biburl = {http://www.bibsonomy.org/bibtex/2e258039867e231fc56bb933950e02f1b/jaeschke}, keywords = {concept triadic fca formal} } @incollection{ksog94tripat, title = {{TRIPAT}: a model for analyzing three-mode binary data}, address = {Berlin}, author = {S. Krolak-Schwerdt and P. Orlik and B. Ganter}, booktitle = {Studies in Classification, Data Analysis, and Knowledge Organization}, editor = {H. H. Bock and W. Lenski and M. M. Richter}, pages = {298--307}, publisher = {Springer}, series = {Information systems and data analysis }, volume = 4, year = 1994, biburl = {http://www.bibsonomy.org/bibtex/22eaf7d1c43191097d5356753acfa1da2/jaeschke}, keywords = {tripat fca concept formal triadic} } @inproceedings{fr01searching, title = {Searching for Objects and Properties with Logical Concept Analysis.}, author = {S. Ferré and O. Ridoux}, booktitle = {Proceedings of the 9th International Conference on Conceptual Structures}, editor = {H. S. Delugach and G. Stumme}, pages = {187-201}, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, volume = 2120, year = 2001, url = {http://dblp.uni-trier.de/db/conf/iccs/iccs2001.html#FerreR01}, ee = {http://link.springer.de/link/service/series/0558/bibs/2120/21200187.htm}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2e0a72edfcc60115f447167dd4978017f/jaeschke}, keywords = {formal file fca system concept} } @incollection{ganter87algorithmen, title = {{Algorithmen zur Formalen Begriffsanalyse}}, author = {B. Ganter}, booktitle = {Beiträge zur Begriffsanalyse }, editor = {B. Ganter and R. Wille and K. E. Wolff}, pages = {241-254}, publisher = {B.I. Wissenschaftsverlag }, year = 1987, biburl = {http://www.bibsonomy.org/bibtex/2298def2cb3fc83ae1ff185763548df53/jaeschke}, keywords = {next closure algorithm concept fca formal} } @book{gw99formal, title = {Formal Concept Analysis: Mathematical Foundations}, author = {B. Ganter and R. Wille}, publisher = {Springer}, year = 1999, biburl = {http://www.bibsonomy.org/bibtex/2ee411290ea5b80d257ac115b2738237c/jaeschke}, keywords = {formal analysis concept fca math} } @inproceedings{wille82restructuring, title = {Restructuring lattice theory: an approach based on hierarchies of concepts. }, address = { Dordrecht--Boston }, author = {Rudolf Wille}, booktitle = { Ordered sets }, editor = {Ivan Rival}, pages = { 445-470 }, publisher = { Reidel }, year = { 1982 }, biburl = {http://www.bibsonomy.org/bibtex/27aca5acbf8f99c811e218ec4b5cf6743/jaeschke}, keywords = {lattice concept formal fca} }