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Volumn 1, Issue 1, 2016, Pages

DCC: a framework for dynamic granular clustering

Author keywords

Dynamic clustering; Granular clustering; Granular computing

Indexed keywords

CLUSTERING ALGORITHMS; GRANULAR COMPUTING;

EID: 84984958855     PISSN: 23644966     EISSN: 23644974     Source Type: Journal    
DOI: 10.1007/s41066-015-0012-z     Document Type: Article
Times cited : (124)

References (48)
  • 4
    • 0142057962 scopus 로고    scopus 로고
    • Chapter: Detecting stable clusters using principal component analysis
    • Humana Press, New York
    • Ben-Hur A, Guyon I (2003) Chapter: Detecting stable clusters using principal component analysis. In: Functional genomics: methods and protocols. Humana Press, New York, pp 160–189
    • (2003) Functional Genomics: Methods and Protocols , pp. 160-189
    • Ben-Hur, A.1    Guyon, I.2
  • 6
    • 84892062680 scopus 로고    scopus 로고
    • A survey of clustering data mining techniques
    • Kogan J, Nicholas C, Teboulle M, (eds), Springer, Berlin
    • Berkhin P (2006) A survey of clustering data mining techniques. In: Kogan J, Nicholas C, Teboulle M (eds) Grouping multidimensional data. Springer, Berlin, pp 25–71 DOI: 10.1007/3-540-28349-8_2
    • (2006) Grouping multidimensional data , pp. 25-71
    • Berkhin, P.1
  • 8
    • 11244353294 scopus 로고    scopus 로고
    • A methodology for dynamic data mining based on fuzzy clustering
    • Crespo F, Weber R (2005) A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets Syst 150(2):267–284 DOI: 10.1016/j.fss.2004.03.028
    • (2005) Fuzzy Sets Syst , vol.150 , Issue.2 , pp. 267-284
    • Crespo, F.1    Weber, R.2
  • 9
    • 0002283033 scopus 로고    scopus 로고
    • From data mining to knowledge discovery in databases
    • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54
    • (1996) AI Mag , vol.17 , Issue.3 , pp. 37-54
    • Fayyad, U.1    Piatetsky-Shapiro, G.2    Smyth, P.3
  • 10
    • 84938808480 scopus 로고    scopus 로고
    • Clustering granular data and their characterization with information granules of higher type
    • Gacek A, Pedrycz W (2015) Clustering granular data and their characterization with information granules of higher type. IEEE Trans Fuzzy Syst 23(4):850–860 DOI: 10.1109/TFUZZ.2014.2329707
    • (2015) IEEE Trans Fuzzy Syst , vol.23 , Issue.4 , pp. 850-860
    • Gacek, A.1    Pedrycz, W.2
  • 11
    • 84874138736 scopus 로고    scopus 로고
    • A survey on learning from data streams: current and future trends
    • Gama J (2012) A survey on learning from data streams: current and future trends. Prog Artif Intell 1(1):45–55 DOI: 10.1007/s13748-011-0002-6
    • (2012) Prog Artif Intell , vol.1 , Issue.1 , pp. 45-55
    • Gama, J.1
  • 12
    • 85107895040 scopus 로고    scopus 로고
    • Gartner says solving ‘Big Data’ challenge involves more than just managing volumes of data (Press Release), retrieved April 21, 2015
    • Gartner Inc., Gartner says solving ‘Big Data’ challenge involves more than just managing volumes of data (Press Release) (2011). (http://www.gartner.com/newsroom/id/1731916, retrieved April 21, 2015)
    • (2011)
  • 13
    • 50149106733 scopus 로고    scopus 로고
    • Dynamic data assigning assessment clustering of streaming data
    • Georgieva O, Klawonn F (2008) Dynamic data assigning assessment clustering of streaming data. Appl Soft Comput 8:1305–1313 DOI: 10.1016/j.asoc.2007.11.006
    • (2008) Appl Soft Comput , vol.8 , pp. 1305-1313
    • Georgieva, O.1    Klawonn, F.2
  • 14
    • 85029114947 scopus 로고    scopus 로고
    • The dynamics of robbery and violence hot spots
    • Herrmann ChR (2015) The dynamics of robbery and violence hot spots. Crime Sci 4(33):1–14
    • (2015) Crime Sci , vol.4 , Issue.33 , pp. 1-14
    • Herrmann, C.R.1
  • 15
    • 84893405732 scopus 로고    scopus 로고
    • Data clustering: a review
    • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323 DOI: 10.1145/331499.331504
    • (1999) ACM Comput Surv , vol.31 , Issue.3 , pp. 264-323
    • Jain, A.K.1    Murty, M.N.2    Flynn, P.J.3
  • 16
    • 0005150228 scopus 로고    scopus 로고
    • Dynamic fuzzy data analysis based on similarity between functions
    • Joentgen A, Mikenina L, Weber R, Zimmermann H-J (1999) Dynamic fuzzy data analysis based on similarity between functions. Fuzzy Sets Syst 105(1):81–90 DOI: 10.1016/S0165-0114(98)00337-6
    • (1999) Fuzzy Sets Syst , vol.105 , Issue.1 , pp. 81-90
    • Joentgen, A.1    Mikenina, L.2    Weber, R.3    Zimmermann, H.-J.4
  • 18
    • 85107885999 scopus 로고    scopus 로고
    • Klimatabelle.info. Klimatabelle Deutschland, retrieved April 19, 2015
    • Klimatabelle.info. Klimatabelle Deutschland (2015). (http://www.klimatabelle.info/europa/deutschland, retrieved April 19, 2015)
    • (2015)
  • 19
    • 84889374621 scopus 로고    scopus 로고
    • Interval computation as an important part of granular computing: an introduction
    • Pedrycz W, Skowron A, Kreinovich V, (eds), Wiley, Chichester
    • Kreinovich V (2008) Interval computation as an important part of granular computing: an introduction. In: Pedrycz W, Skowron A, Kreinovich V (eds) Handbook of granular computing. Wiley, Chichester, pp 1–31 DOI: 10.1002/9780470724163.ch1
    • (2008) Handbook of granular computing , pp. 1-31
    • Kreinovich, V.1
  • 20
    • 0027595430 scopus 로고
    • A possibilistic approach to clustering
    • Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110 DOI: 10.1109/91.227387
    • (1993) IEEE Trans Fuzzy Syst , vol.1 , Issue.2 , pp. 98-110
    • Krishnapuram, R.1    Keller, J.M.2
  • 22
    • 83455199158 scopus 로고    scopus 로고
    • Research of hierarchical clustering based on dynamic granular computing
    • Li XY, Sun JX, Gao GH, Fu JH (2011) Research of hierarchical clustering based on dynamic granular computing. J Comput 6(12):2526–2533
    • (2011) J Comput , vol.6 , Issue.12 , pp. 2526-2533
    • Li, X.Y.1    Sun, J.X.2    Gao, G.H.3    Fu, J.H.4
  • 23
    • 3042789571 scopus 로고    scopus 로고
    • Interval set clustering of web users with rough k-means
    • Lingras P, West C (2004) Interval set clustering of web users with rough k-means. J Intell Inf Syst 23:5–16 DOI: 10.1023/B:JIIS.0000029668.88665.1a
    • (2004) J Intell Inf Syst , vol.23 , pp. 5-16
    • Lingras, P.1    West, C.2
  • 25
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley symposium, pp 281–297
    • (1967) Fifth Berkeley Symposium , pp. 281-297
    • Macqueen, J.B.1
  • 27
    • 84861734734 scopus 로고    scopus 로고
    • Community detection in Social Media—performance and application considerations
    • Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P (2012) Community detection in Social Media—performance and application considerations. Data Min Knowl Discov 24:515–554 DOI: 10.1007/s10618-011-0224-z
    • (2012) Data Min Knowl Discov , vol.24 , pp. 515-554
    • Papadopoulos, S.1    Kompatsiaris, Y.2    Vakali, A.3    Spyridonos, P.4
  • 28
    • 27744565978 scopus 로고
    • Rough sets
    • Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356 DOI: 10.1007/BF01001956
    • (1982) Int J Comput Inf Sci , vol.11 , pp. 341-356
    • Pawlak, Z.1
  • 29
    • 0031996836 scopus 로고    scopus 로고
    • Shadowed sets: representing and processing fuzzy sets
    • Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B: Cybern 28:103–109 DOI: 10.1109/3477.658584
    • (1998) IEEE Trans Syst Man Cybern Part B: Cybern , vol.28 , pp. 103-109
    • Pedrycz, W.1
  • 31
    • 46749112873 scopus 로고    scopus 로고
    • Granular computing—the emerging paradigm
    • Pedrycz W (2007) Granular computing—the emerging paradigm. J Uncertain Syst 1(1):38–61
    • (2007) J Uncertain Syst , vol.1 , Issue.1 , pp. 38-61
    • Pedrycz, W.1
  • 33
    • 84861189820 scopus 로고    scopus 로고
    • An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering
    • Pedrycz W, Bargiela A (2012) An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering. IEEE Trans Syst Man Cybern Part B: Cybern 42(3):582–590 DOI: 10.1109/TSMCB.2011.2170067
    • (2012) IEEE Trans Syst Man Cybern Part B: Cybern , vol.42 , Issue.3 , pp. 582-590
    • Pedrycz, W.1    Bargiela, A.2
  • 35
    • 84901803247 scopus 로고    scopus 로고
    • Rough clustering utilizing the principle of indifference
    • Peters G (2014) Rough clustering utilizing the principle of indifference. Inf Sci 277:358–374 DOI: 10.1016/j.ins.2014.02.073
    • (2014) Inf Sci , vol.277 , pp. 358-374
    • Peters, G.1
  • 36
    • 84864780110 scopus 로고    scopus 로고
    • Dynamic clustering with soft computing
    • Peters G, Weber R (2012) Dynamic clustering with soft computing. WIREs Data Min Knowl Discov 2(3):226–236 DOI: 10.1002/widm.1050
    • (2012) WIREs Data Min Knowl Discov , vol.2 , Issue.3 , pp. 226-236
    • Peters, G.1    Weber, R.2
  • 37
    • 84864759981 scopus 로고    scopus 로고
    • Dynamic rough clustering and its applications
    • Peters G, Weber R, Nowatzke R (2012) Dynamic rough clustering and its applications. Appl Soft Comput 12:3193–3207 DOI: 10.1016/j.asoc.2012.05.015
    • (2012) Appl Soft Comput , vol.12 , pp. 3193-3207
    • Peters, G.1    Weber, R.2    Nowatzke, R.3
  • 38
    • 84873284300 scopus 로고    scopus 로고
    • Soft clustering—fuzzy and rough approaches and their extensions and derivatives
    • Peters G, Crespo F, Lingras P, Weber R (2013) Soft clustering—fuzzy and rough approaches and their extensions and derivatives. Int J Approx Reason 54(2):307–322 DOI: 10.1016/j.ijar.2012.10.003
    • (2013) Int J Approx Reason , vol.54 , Issue.2 , pp. 307-322
    • Peters, G.1    Crespo, F.2    Lingras, P.3    Weber, R.4
  • 41
    • 16444383160 scopus 로고    scopus 로고
    • A survey of clustering algorithms
    • Xu R, Wunsch D (2005) A survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678 DOI: 10.1109/TNN.2005.845141
    • (2005) IEEE Trans Neural Netw , vol.16 , Issue.3 , pp. 645-678
    • Xu, R.1    Wunsch, D.2
  • 44
    • 44649147862 scopus 로고    scopus 로고
    • Granular computing: Past, present, and future
    • Springer, Berlin
    • Yao YY, (2008a) Granular computing: past, present, and future. In: Rough set and knowledge technology (RSKT 2008). LNAI, vol 5009. Springer, Berlin, pp 27–28
    • (2008) Rough Set and Knowledge Technology (RSKT 2008). LNAI , vol.5009 , pp. 27-28
    • Yao, Y.Y.1
  • 45
    • 70349984171 scopus 로고    scopus 로고
    • A unified framework of granular computing
    • Pedrycz W, Skowron A, Kreinovich V, (eds), Wiley, Chichester
    • Yao YY (2008b) A unified framework of granular computing. In: Pedrycz W, Skowron A, Kreinovich V (eds) Handbook of granular computing. Wiley, Chichester, pp 401–410 DOI: 10.1002/9780470724163.ch17
    • (2008) Handbook of granular computing , pp. 401-410
    • Yao, Y.Y.1
  • 46
    • 34248666540 scopus 로고
    • Fuzzy sets
    • Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353 DOI: 10.1016/S0019-9958(65)90241-X
    • (1965) Inf Control , vol.8 , Issue.3 , pp. 338-353
    • Zadeh, L.1
  • 48
    • 33750351856 scopus 로고    scopus 로고
    • Generalized theory of uncertainty (GTU)—principal concepts and ideas
    • Zadeh L (2006) Generalized theory of uncertainty (GTU)—principal concepts and ideas. Comput Stat Data Anal 51(1):15–46 DOI: 10.1016/j.csda.2006.04.029
    • (2006) Comput Stat Data Anal , vol.51 , Issue.1 , pp. 15-46
    • Zadeh, L.1


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