%0 
%0 Conference Proceedings
%A Berendt, Bettina; Hotho, Andreas & Stumme, Gerd
%D 2002
%T Towards Semantic Web Mining
%E Horrocks, I. & Hendler, J.
%B Proceedings of the First International Semantic Web Conference on The Semantic Web
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%I Springer
%V 2342
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%P 264-278
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%S Lecture Notes in Computer Science
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%F berendt02semweb
%K cites.gradu mrefs research.mining research.sw 
%X Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.
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%U http://www.springerlink.com/link.asp?id=w3gp1h0tgaup9h58
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%0 Journal Article
%A Cañas, Alberto J.; Carvalho, Marco & Arguedas, Marco
%D 2002
%T Mining the Web to Suggest Concepts during Concep Mapping
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%K mrefs research.conceptual.generation research.conceptual.graphs research.kr.ontologies research.mining 
%X The most challenging aspect of constructing a concept map is not coming up with the list of concepts to include, but linking the concepts into meaningful propositions creating a coherent structure that reflects the learner’s understanding of a domain. We present an algorithm that, during the process of concept mapping, takes the partially constructed map as input to mine the web, and presents to the user a list of suggested concepts that are relevant to the map under construction. Testing a preliminary implementation of the algorithm with a set of users during a concept-mapping workshop seems to validate its viability. Depending on the size of the suggestion list, the algorithm presented on average between 47% and 69% of the concepts in the final maps before the users added them to the map, showing that the algorithm is able to retrieve concepts relevant to the concept mapping effort
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%U http://www.ihmc.us/users/acanas/Publications/ConceptSuggester/ConceptSuggesterSBIE2002.htm
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%0 Journal Article
%A Dustdar, Schahram & Hoffmann, Thomas
%D 2007
%T Interaction pattern detection in process oriented information systems
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%B Data & Knowledge Engineering
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%V 62
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%N 1
%P 138--155
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%F dustdar07interaction
%K mrefs research.bizInt.bpm research.conf.se2008 research.mining 
%X Finding interaction patterns is a challenging problem, but this kind of information about processes or social networks might be useful for an organization's management to understand the role of specific persons in processes. Ad-hoc processes are of special interest, because they result from runtime-collaboration between the participants, not using predefined models specifying the persons responsibilities and the order of activities. Because social network analysis (SNA) is closely related to interaction pattern detection, we introduce it as a method to determine properties of social networks like project teams. In order to support the detection of these patterns, we discuss the necessity of additional semantic activity information, and we propose rules and an algorithm that allow detecting such patterns automatically. We apply our algorithm in a case study, using Caramba to perform an example ad-hoc process.
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%U http://dx.doi.org/10.1016/j.datak.2006.07.010
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%A Grigori, Daniela; Casati, Fabio; Castellanos, Malu; Dayal, Umeshwar; Sayal, Mehmet & Shan, Ming-Chien
%D 2004
%T Business Process Intelligence
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%B Computers in Industry
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%V 53
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%N 3
%P 321--343
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%K cites.pclass cites.procm mrefs research.bizInt.bpm research.mining 
%X Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.
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%U http://dx.doi.org/10.1016/j.compind.2003.10.007
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%0 Thesis
%A Nurminen, Miika
%D 2005
%T Tiedonlouhinta rakenteisista dokumenteista
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%I University of Jyväskylä
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%K mrefs research.clustering research.ir research.mining research.papers research.xml 
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%U http://thesis.jyu.fi/05/URN_NBN_fi_jyu-200594.pdf
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%A Nurminen, Miika; Honkaranta, Anne & K\"arkk\"ainen, Tommi
%D 2005
%T ExtMiner: Combining Multiple Ranking and Clustering Algorithms for Structured Document Retrieval
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%B International workshop on Integrating Data Mining, Databases and Information Retrieval (IDDI'05), Proceedings of the 16th International Workshop on Database and Expert Systems Applications
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%I IEEE Computer Society
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%P 1036-1040
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%F nurminen05extminer
%K cites.dss.r cites.procm mrefs research.clustering research.ir research.ir.ranking research.mining research.papers research.xml 
%X  This paper introduces ExtMiner, a platform and potential tool for information management in SMEs (small & medium-size enterprise), or for organizational workgroups. ExtMiner supports interactive and iterative clustering of documents. It provides users with a visual cluster and list views at the same time, supporting iterative search process. ExtMiner may also be applied as a platform for research on retrieval fusion, since it combines search, clustering and visualization algorithms. ExtMiner was evaluated with three document collections. Although the findings were encouraging the user interface and performance with large document repositories need further development.
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%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1508411
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%0 Conference Proceedings
%A Nurminen, Miika; Honkaranta, Anne & Kärkkäinen, Tommi
%D 2007
%T ProcMiner: Advancing Process Analysis and Management
%E 
%B Workshop on Text Data Mining and Management (TDMM), IEEE 23rd Int. Conf. on Data Engineering
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%I IEEE
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%P 760-769
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%K cites.dss.r cites.pclass cites.ucot mrefs out.gcp research.bizInt.bpm research.mining research.papers research.xml 
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%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4401065
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%0 Conference Proceedings
%A Srinivasa, K.G.; Jagadish, M.; Venugopal, K.R. & Patnaik, L.M.
%D 2007
%T Data Mining based Query Processing using Rough Sets and Genetic Algorithms
%E 
%B Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
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%P 275-282
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%F srinivasa07query
%K mrefs research.conceptual.generation research.ir research.mining 
%X The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good
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%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4221308
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%0 Journal Article
%A Stumme, Gerd
%D 2003
%T Off to new shores: conceptual knowledge discovery and processing
%E 
%B International Journal of Human-Computer Studies
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%V 59
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%N 3
%P 287--325
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%K mrefs research.conceptual research.kr.ontologies research.mining 
%X In the last years, the main orientation of formal concept analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyses why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis et al. (1993). It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and ontology engineering. The second part of the article discusses three lines of research which witness the new orientation of FCA: FCA as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.
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%U http://www.sciencedirect.com/science/article/B6WGR-48M7V77-1/2/3e2aeca1e3bf2240d301a815012fe2ed
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%0 Journal Article
%A Xu, Feifei; Yao, Yiyu & Miao, Duoqian
%D 2008
%T Rough Set Approximations in Formal Concept Analysis and Knowledge Spaces
%E 
%B Foundations of Intelligent Systems
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%P 319--328
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%K mrefs research.conceptual.graphs research.mining science.math 
%X This paper proposes a generalized definition of rough set approximations, based on a subsystem of subsets of a universe. The subsystem is not assumed to be closed under set complement, union and intersection. The lower or upper approximation is nolonger one set but composed of several sets. As special cases, approximations in formal concept analysis and knowledge spacesare examined. The results provide a better understanding of rough set approximations.
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%U http://dx.doi.org/10.1007/978-3-540-68123-6_35
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