McLennan M, Kennell R (2010) HUBzero: a platform for dissemination and collaboration in computational science and engineering. Comput Sci Eng 12:48–53
Article
Google Scholar
Klimeck G, McLennan M, Brophy SP, Adams GB III, Lundstrom MS (2008) nanohub.org: Advancing education and research in nanotechnology. Comput Sci Eng 10(5):17–23
Article
Google Scholar
Suh Y-K, Ryu H, Kim H, Cho KW (2016) EDISON: a web-based HPC simulation execution framework for large-scale scientific computing software. In: Proceedings of the 16th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid), IEEE, Piscataway, pp 608–612
Pardamean B, Baurley JW, Perbangsa AS, Utami D, Rijzaani H, Satyawan D (2018) Information technology infrastructure for agriculture genotyping studies. J Inf Process Syst 14(3):655–665
Google Scholar
W3C PROV: PROV-Overview. https://www.w3.org/TR/prov-overview/. Accessed Jan 28 2018
Moreau L, Freire J, Futrelle J, McGrath RE, Myers J, Paulson P (2008) The open provenance model: an overview. In: International provenance and annotation workshop, Springer, Berlin, pp 323–326
Moreau L, Clifford B, Freire J, Futrelle J, Gil Y, Groth P, Kwasnikowska N, Miles S, Missier P, Myers J (2011) The open provenance model core specification (v1. 1). Future Gener Comput Syst 27(6):743–756
Article
Google Scholar
Herschel M, Diestelkämper R, Ben Lahmar H (2017) A survey on provenance: What for? What form? What from? Int J Very Large Data Bases (VLDB Journal) 26(6):881–906
Article
Google Scholar
Tylissanakis G, Cotronis Y (2009) Data provenance and reproducibility in grid based scientific workflows. In: Proceedings of the 2009 workshops at the grid and pervasive computing conference, IEEE, Piscataway, pp 42–49
Simmhan YL, Plale B, Gannon D (2006) A framework for collecting provenance in data-centric scientific workflows. In: Proceedings of the international conference on web services, IEEE, Piscataway, pp 427–436
Bavoil L, Callahan SP, Crossno PJ, Freire J, Scheidegger CE, Silva CT, Vo HT (2005) Vistrails: enabling interactive multiple-view visualizations. In: IEEE visualization (VIS), IEEE, Piscataway, pp 135–142
Freire J, Silva C The official website for VisTrails. https://www.vistrails.org/index.php/Main_Page. Accessed Feb 5 2018
Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock MR, Wipat A (2004) Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17):3045–3054
Article
Google Scholar
Apache Taverna: Apache Taverna. https://taverna.incubator.apache.org/. Accessed Mar 2 2018
Montali M, Pesic M, van der Aalst WM, Chesani F, Mello P, Storari S (2010) Declarative specification and verification of service choreographiess. ACM Trans Web 4:1–62
Article
Google Scholar
Altintas I, Berkley C, Jaeger E, Jones M, Ludascher B, Mock S (2004) Kepler: an extensible system for design and execution of scientific workflows. In: Proceedings of the 16th international conference on scientific and statistical database management (SSDBM), IEEE, Piscataway, pp 423–424
Caron E, Desprez F, Muresan A (2010) Forecasting for grid and cloud computing on-demand resources based on pattern matching. In: Proceedings of the second international conference on cloud computing technology and science, IEEE, Piscataway, pp 456–463
Li X, Joshi C, Tan AYS, Ko RKL (2015) Inferring user actions from provenance logs. In: Trustcom/BigDataSE/ISPA, 2015, vol 1. IEEE, Piscataway, pp 742–749
Malik MJ, Fahringer T, Prodan R (2013) Execution time prediction for grid infrastructures based on runtime provenance data. In: Proceedings of the 8th workshop on workflows in support of large-scale science, ACM, New York, pp 48–57
Hiden H, Woodman S, Watson P (2016) Prediction of workflow execution time using provenance traces: practical applications in medical data processing. In: Proceedings of the 12th international conference on eScience, IEEE, Piscataway, pp 21–30
Danger R, Joy RC, Darlington J, Curcin V (2012) Access control for OPM provenance graphs. In: International provenance and annotation workshop, Springer, Berlin, pp 233–235
Freitas A, Knap T, O’Riain S, Curry E (2011) W3P: building an OPM based provenance model for the web. Future Gener Comput Syst 27(6):766–774
Article
Google Scholar
Shu Y, Taylor K, Hapuarachchi P, Peters C (2012) Modelling provenance in hydrologic science: a case study on streamflow forecasting. J Hydroinf 14(4):944–959
Article
Google Scholar
Ebden M, Huynh TD, Moreau L, Ramchurn S. Roberts S (2012) Network analysis on provenance graphs from a crowdsourcing application. In: International provenance and annotation workshop, Springer, Berlin, pp 168–182
Glatard T, Lartizien C, Gibaud B, Da Silva RF, Forestier G, Cervenansky F, Alessandrini M, Benoit-Cattin H, Bernard O, Camarasu-Pop S (2013) A virtual imaging platform for multi-modality medical image simulation. IEEE Trans Med Imaging 32(1):110–118
Article
Google Scholar
Jung IY, Eom H, Yeom HY (2011) Multi-layer trust reasoning on open provenance model for e-Science environment. In: IEEE 9th International symposium on parallel and distributed processing with applications (ISPA), IEEE, Piscataway, pp 294–299
Gehani A, Tariq D (2012) SPADE: support for provenance auditing in distributed environments. In: Proceedings of the 13th international middleware conference, Springer, New York, pp 101–120
Zhao D, Shou C, Malik T, Raicu I (2013) Distributed data provenance for large-scale data-intensive computing. In: IEEE international conference on cluster computing (CLUSTER), IEEE, Piscataway, pp 1–8
Belhajjame K, B’Far R, Cheney J, Coppens S, Cresswell S, Gil Y, Groth P, Klyne G, Lebo T, McCusker J et al (2013) PROV-DM: The PROV Data Model
Pignotti E, Polhill G, Edwards P (2013) Using provenance to analyse agent-based simulations. In: Proceedings of the joint EDBT/ICDT 2013 workshops, ACM, New York, pp 319–322
Suh Y-K, Ma J (2017) SuperMan: a novel system for storing and retrieving scientific-simulation provenance for efficient job executions on computing clusters. In: 2017 IEEE 2nd international workshops on foundations and applications of Self* Systems (FAS* W), IEEE, Piscataway, pp 283–288
Cohen-Boulakia S, Biton O, Cohen S, Davidson S (2008) Addressing the provenance challenge using ZOOM. Concurr Comput Pract Exp 20(5):497–506
Article
Google Scholar
Doerr M, Theodoridou M (2011) CRM\(_{dig}\): a generic digital provenance model for scientific observation. TaPP 11:20–21
Google Scholar
Doerr M (2003) The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata. AI Mag 24(3):75
Google Scholar
Doerr M, Ore C-E, Stead S (2007) The CIDOC conceptual reference model: a new standard for knowledge sharing. In: Tutorials, posters, panels and industrial contributions at the 26th international conference on conceptual modeling, vol 83. Australian Computer Society, Inc, Australia, pp 51–56
Theodoridou M, Tzitzikas Y, Doerr M, Marketakis Y, Melessanakis V (2010) Modeling and querying provenance by extending CIDOC CRM. Distrib Parallel Databases 27(2):169–210
Article
Google Scholar
Gerhards M, Sander V, Matzerath T, Belloum A, Vasunin D, Benabdelkader A (2011) Provenance opportunities for WS-VLAM: an exploration of an e-Science and an e-Business approach. In: Proceedings of the 6th workshop on workflows in support of large-scale science, ACM, New York, pp 57–66
OASIS: OASIS Web Services Resource Framework (WSRF) TC. https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=wsrf. Accessed Mar 11 2018
OASIS: OASIS Web Services Notification (WSN) TC. https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=wsn. Accessed Mar 11 2018
Groth P, Luck M, Moreau L (2004) A protocol for recording provenance in service-oriented grids. In: International conference on principles of distributed systems (OPODIS), vol 3544. Springer, Berlin, pp 124–139
Erl T (2005) Service-oriented architecture: concepts, technology, and design. Prentice Hall PTR, Upper Saddle River
Google Scholar
Sun F, Zhao J, Gomadam K, Prasanna VK (2010) Provenance collection in reservoir management workflow environments. In: Proceedings of the 7th international conference on information technology: new generations, IEEE, Piscataway, pp 82–87
Kloss GK, Schreiber A (2006) Provenance implementation in a scientific simulation environment. In: International provenance and annotation workshop, Springer, Berlin, pp 37–45
Gaspar W, Braga RM, Campos F (2011) SciProv: an architecture for semantic query in provenance metadata on e-Science context. In: ITBAM, Springer, Berlin, pp 68–81
Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43
Article
Google Scholar
Lopez V, Fernández M, Motta E, Stieler N (2012) Poweraqua: supporting users in querying and exploring the semantic web. Semant web 3(3):249–265
Google Scholar
Pérez J, Arenas M, Gutierrez C (2009) Semantics and complexity of sparql. ACM Trans Database Syst (TODS) 34(3):16
Article
Google Scholar
Woodman S, Hiden H, Watson P, Missier P (2011) Achieving reproducibility by combining provenance with service and workflow versioning. In: Proceedings of the 6th workshop on workflows in support of large-scale science, ACM, New York, pp 127–136
Hiden H, Watson P, Woodman S, Leahy D (2011) e-Science central: cloud-based e-Science and its application to chemical property modelling. Relatório Técnico CS-TR-1227, School of Comp. Sci. Newcastle University
Zhao J, Klyne G, Shotton D (2008) Provenance and linked data in biological data webs. In: Proceedings of the WWW2008 workshop on linked data on the web (LDOW 2008)
Wylot M, Cudre-Mauroux P, Groth P (2014) TripleProv: efficient processing of lineage queries in a native RDF store. In: Proceedings of the 23rd international conference on world wide web, ACM, New York, pp 455–466
Wylot M, Cudre-Mauroux P, Groth P (2015) Executing provenance-enabled queries over web data. In: Proceedings of the 24th international conference on world wide web, International World Wide Web Conference Committee, Geneva, pp 1275–1285
Wylot M, Cudré-Mauroux P, Groth P (2015) A demonstration of TripleProv: tracking and querying provenance over web data. Proc VLDB Endow 8(12):1992–1995
Article
Google Scholar
Wylot M, Cudre-Maroux P, Hauswirth M, Groth P (2017) Storing, tracking, and querying provenance linked data. IEEE Trans Knowl Data Eng 29:1751–1764
Article
Google Scholar
W3C PROV: PROV-AQ: Provenance Access and Query. https://www.w3.org/TR/prov-aq/. Accessed Mar 13 2018
Chen P, Plale B, Cheah Y-W, Ghoshal D, Jensen S, Luo Y (2012) Visualization of network data provenance. In: Proceedings of the 19th international conference on high performance computing (HiPC), IEEE, Piscataway, pp 1–9
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Article
Google Scholar
Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker T (2010) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431–432
Article
Google Scholar
Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD (2010) Cytoscape web: an interactive web-based network browser. Bioinformatics 26(18):2347–2348
Article
Google Scholar
Horta F, Dias J, Elias R, Oliveira D, Coutinho A, Mattoso M (2013) Prov-Vis: Large-scale scientific data visualization using provenance. In: Proceedings of the international conference on high performance computing, networking, storage and analysis, Denver
de Oliveira D, Ogasawara E, Baião F, Mattoso M (2010) Scicumulus: a lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In: IEEE 3rd international conference on cloud computing (CLOUD), IEEE, Piscataway, pp 378–385
Ogasawara E, Dias J, Silva V, Chirigati F, Oliveira D, Porto F, Valduriez P, Mattoso M (2013) Chiron: a parallel engine for algebraic scientific workflows. Concurr Comput Pract Exp 25(16):2327–2341
Article
Google Scholar
Jensen S, Plale B, Aktas MS, Luo Y, Chen P, Conover H (2013) Provenance capture and use in a satellite data processing pipeline. IEEE Trans Geosci Remote Sens 51(11):5090–5097
Article
Google Scholar
Simmhan YL, Plale B, Gannon D, Marru S (2006) Performance evaluation of the Karma provenance framework for scientific workflows. In: International provenance and annotation workshop (IPAW’06), Springer, Berlin, pp 222–236
Howe B, Lawson P, Bellinger R, Anderson E, Santos E, Freire J, Scheidegger C, Baptista A, Silva C (2008) End-to-end eScience: integrating workflow, query, visualization, and provenance at an ocean observatory. In: Proceedings of IEEE fourth international conference on eScience, IEEE, Piscataway, pp 127–134
Callahan SP, Freire J, Santos E, Scheidegger CE, Silva CT, Vo HT (2006) VisTrails: visualization meets data management. In: Proceedings of the 2006 ACM SIGMOD international conference on management of data, ACM, New York, pp 745–747
Naseri M, Ludwig SA (2013) Extracting workflow structures through Bayesian learning and provenance data. In: Proceedings of the 13th international conference on intelligent systems design and applications, IEEE, Piscataway, pp 319–324
De Campos CP, Zeng Z, Ji Q (2009) Structure learning of Bayesian networks using constraints. In: Proceedings of the 26th annual international conference on machine learning, ACM, New York, pp 113–120
Campos CP, Ji Q (2011) Efficient structure learning of Bayesian networks using constraints. J Mach Learn Res 12:663–689
MathSciNet
MATH
Google Scholar
Zhang J, Liu Q, Xu K (2009) FlowRecommender: a workflow recommendation technique for process provenance. In: Proceedings of the eighth Australasian data mining conference, vol 101, Australian Computer Society, Inc, Australia, pp 55–61
De Oliveira FT, Murta L, Werner C, Mattoso M (2008) Using provenance to improve workflow design. In: International provenance and annotation workshop, Springer, Berlin, pp 136–143
Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems, vol. 4321. 2nd edn. Springer, Berlin, Lecture Notes in Computer Science, pp 291–324
Garijo D, Corcho O, Gil Y (2013) Detecting common scientific workflow fragments using templates and execution provenance. In: Proceedings of the seventh international conference on knowledge capture, ACM, New York, pp 33–40
Zeng R, He X, van der Aalst WM (2011) A method to mine workflows from provenance for assisting scientific workflow composition. In: IEEE world congress on services, IEEE, Piscataway, pp 169–175
Silva MF, Baião FA, Revoredo K (2014) Towards planning scientific experiments through declarative model discovery in provenance data. In: Proceedings of IEEE 10th international conference on eScience, vol. 2. IEEE, Piscataway, pp 95–98
Pesic M, Schonenberg H, Van der Aalst WM (2007) Declare: full support for loosely-structured processes. In: 11th IEEE international enterprise distributed object computing conference (EDOC), IEEE, Piscataway, p 287
DeBoer D, Zhou W, Singh L (2013) Using substructure mining to identify misbehavior in network provenance graphs. In: First international workshop on graph data management experiences and systems, ACM, New York, p 6
Missier P (2011) Incremental workflow improvement through analysis of its data provenance. In: TaPP
Altintas I, Barney O, Jaeger-Frank E (2006) Provenance collection support in the Kepler scientific workflow system. In: International provenance and annotation workshop, Springer, Berlin, pp 118–132
Ludäscher B, Altintas I, Berkley C, Higgins D, Jaeger E, Jones M, Lee EA, Tao J, Zhao Y (2006) Scientific workflow management and the Kepler system. Concurr Comput Pract Exp 18(10):1039–1065
Article
Google Scholar
Ko RK, Will MA (2014) Progger: an efficient, tamper-evident Kernel-space logger for cloud data provenance tracking. In: Proceedings of the 7th international conference on cloud computing (CLOUD), IEEE, Piscataway, pp 881–889
Dai D, Chen Y, Kimpe D, Ross R (2014) Provenance-based prediction scheme for object storage system in HPC. In: Proceedings of the 14th IEEE/ACM international symposium on cluster, cloud and grid computing, IEEE, Piscataway, pp 550–551
Alpaydin E (2010) Introduction to machine learning, 2nd edn. The MIT Press, Cambridge
MATH
Google Scholar
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52
Article
Google Scholar
Chen P (2016) Big data analytics in static and streaming provenance. Ph.D. thesis, Indiana University
Macko P, Margo D, Seltzer M (2013) Local clustering in provenance graphs. In: Proceedings of the 22nd ACM international conference on information and knowledge management, ACM, New York, pp 835–840
Ainy E, Bourhis P, Davidson SB, Deutch D, Milo T (2015) Approximated summarization of data provenance. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, New York, pp 483–492
Groth P, Gil Y, Magliacane S (2012) Automatic metadata annotation through reconstructing provenance. In: Semantic web in provenance management workshop
Borne K (2009) Scientific data mining in astronomy. arXiv preprint arXiv: 0911.0505
Stevens RD, Robinson AJ, Goble CA (2003) myGrid: personalised bioinformatics on the information grid. Bioinformatics 19(suppl–1):302–304
Article
Google Scholar
Foster I, Vockler J, Wilde M, Zhao Y (2002) Chimera: a virtual data system for representing, querying, and automating data derivation. In: Proceedings of the 14th international conference on scientific and statistical database management, IEEE, Piscataway, pp 37–46
Pancerella C, Hewson J, Koegler W, Leahy D, Lee M, Rahn L, Yang C, Myers JD, Didier B, McCoy R (2003) Metadata in the collaboratory for multi-scale chemical science. In: International conference on Dublin core and metadata applications, Pancerella, Shillington, pp 121–129
Miles S, Wong SC, Fang W, Groth P, Zauner K-P, Moreau L (2007) Provenance-based validation of e-Science experiments. Web Semant Sci Serv Agents World Wide Web 5(1):28–38
Article
Google Scholar
Moreau L, Groth P, Miles S, Vazquez-Salceda J, Ibbotson J, Jiang S, Munroe S, Rana O, Schreiber A, Tan V (2008) The provenance of electronic data. Commun ACM 51(4):52–58
Article
Google Scholar
Groth P, Miles S, Moreau L (2009) A model of process documentation to determine provenance in mash-ups. ACM Trans Internet Technol (TOIT) 9(1):3
Article
Google Scholar
Groth P, Moreau L (2009) Recording process documentation for provenance. IEEE Trans Parallel Distrib Syst 20(9):1246–1259
Article
Google Scholar
Miles S, Groth P, Branco M, Moreau L (2007) The requirements of using provenance in e-Science experiments. J Grid Comput 5(1):1–25
Article
Google Scholar
Miles S, Groth P, Munroe S, Moreau L (2011) PrIMe: a methodology for developing provenance-aware applications. ACM Trans Softw Eng Methodol (TOSEM) 20(3):8
Article
Google Scholar
Frew J, Bose R (2001) Earth system science workbench: a data management infrastructure for earth science products. In: Proceedings of the thirteenth international conference on scientific and statistical database management (SSDBM), IEEE, Piscataway, pp 180–189
Crawl D, Wang J, Altintas I (2011) Provenance for MapReduce-based data-intensive workflows. In: Proceedings of the 6th workshop on workflows in support of large-scale science (WORKS’11), ACM, New York, pp 21–30
Ikeda R, Park H, Widom J (2011) Provenance for generalized map and reduce workflows. In: Proceedings of the fifth biennial conference on innovative data systems research (CIDR), Asilomar, pp 273–283
Akoush S, Sohan R, Hopper A (2013) HadoopProv: towards provenance as a first class citizen in MapReduce. In: TaPP
Amsterdamer Y, Davidson SB, Deutch D, Milo T, Stoyanovich J, Tannen V (2011) Putting lipstick on pig: enabling database-style workflow provenance. Proc VLDB Endow 5(4):346–357
Article
Google Scholar
Cheung K-H, Hager J, Pan D, Srivastava R, Mane S, Li Y, Miller P, Williams KR (2004) KARMA: a web server application for comparing and annotating heterogeneous microarray platforms. Nucleic Acids Res 32(suppl–2):441–444
Article
Google Scholar
Deelman E, Blythe J, Gil Y, Kesselman C, Mehta G, Patil S, Su M-H, Vahi K, Livny M (2004) Pegasus: mapping scientific workflows onto the grid. In: Grid computing, Springer, Berlin, pp 11–20
Deelman E, Singh G, Su M-H, Blythe J, Gil Y, Kesselman C, Mehta G, Vahi K, Berriman GB, Good J (2005) Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci Program 13(3):219–237
Google Scholar
Deelman E, Vahi K, Juve G, Rynge M, Callaghan S, Maechling PJ, Mayani R, Chen W, da Silva RF, Livny M (2015) Pegasus, a workflow management system for science automation. Future Gener Comput Syst 46:17–35
Article
Google Scholar
Barga RS, Digiampietri LA (2008) Automatic capture and efficient storage of e-Science experiment provenance. Concurr Comput Pract Exp 20(5):419–429
Article
Google Scholar
Wilde M, Hategan M, Wozniak JM, Clifford B, Katz DS, Foster I (2011) Swift: a language for distributed parallel scripting. Parallel Comput 37(9):633–652
Article
Google Scholar
Gadelha LM Jr, Clifford B, Mattoso M, Wilde M, Foster I (2011) Provenance management in Swift. Future Gener Comput Syst 27(6):775–780
Article
Google Scholar
University of Chicago Computation Institute: The Swift Project. www.ci.uchicago.edu/swift. Accessed Mar 5 2018
Macko P, Chiarini M, Seltzer M (2011) Collecting provenance via the Xen Hypervisor. In: TaPP
Hammad R, Wu C-S (2014) Provenance as a service: a data-centric approach for real-time monitoring. In: 2014 IEEE international congress on big data (BigData Congress), IEEE, Piscataway, pp 258–265
Cheah Y-W, Canon R, Plale B, Ramakrishnan L (2013) Milieu: lightweight and configurable big data provenance for science. In: Big data (BigData Congress), 2013 IEEE International Congress, IEEE, Piscataway, pp 46–53
Davison A (2012) Automated capture of experiment context for easier reproducibility in computational research. Comput Sci Eng 14(4):48–56
Article
Google Scholar
Davison AP, Mattioni M, Samarkanov D, Teleńczuk B (2014) Sumatra: a toolkit for reproducible research. In: Implementing reproducible research. CRC Press, Boca Raton, pp 57–79
Google Scholar
Hiden H, Woodman S, Watson P, Cala J (2013) Developing cloud applications using the e-Science central platform. Phil Trans R Soc A 371(1983):20120085
Article
Google Scholar
Watson P, Hiden H, Woodman S (2010) e-Science central for CARMEN: science as a service. Concurr Comput Pract Exp 22(17):2369–2380
Article
Google Scholar
Ayachit U (2015) The Paraview guide: a parallel visualization application
Oracle Corporation: MySQL: The World’s Most Popular Open Source Database. https://www.mysql.com/. Accessed Mar 22 2018
Olston C, Reed B, Srivastava U, Kumar R, Tomkins A (2008) Pig latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, ACM, New York, pp 1099–1110
Olson MA, Bostic K, Seltzer MI Berkeley DB (1999) In: USENIX annual technical conference, FREENIX track, pp 183–191
Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Mining Knowl Discov 15(1):55–86
Article
MathSciNet
Google Scholar
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc., San Francisco
MATH
Google Scholar
Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892
Article
MATH
Google Scholar
Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26(4):354–359
Article
MATH
Google Scholar
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. SIGKDD 96:226–231
Google Scholar
Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13(6):47–60
Article
Google Scholar
Hao F, Sim DS, Park DS, Seo HS (2017) Similarity evaluation between graphs: a formal concept analysis approach. J Inf Process Syst 13(5):1158–1167
Google Scholar