메뉴 건너뛰기




Volumn 4, Issue 1, 2012, Pages 46-56

Fast, linear time, m-adic hierarchical clustering for search and retrieval using the Baire metric, with linkages to generalized ultrametrics, hashing, formal concept analysis, and precision of data measurement

Author keywords

algorithm; clustering; computational complexity; data analysis; metric; retrieval; search; storage; ultrametric

Indexed keywords


EID: 84966728646     PISSN: 20700466     EISSN: 20700474     Source Type: Journal    
DOI: 10.1134/S2070046612010062     Document Type: Article
Times cited : (11)

References (45)
  • 2
    • 0003910106 scopus 로고
    • Multidimensional clustering algorithms
    • F. Murtagh, “Multidimensional clustering algorithms,” Physica-Verlag (1985).
    • (1985) Physica-Verla
    • Murtagh, F.1
  • 3
    • 17544364135 scopus 로고    scopus 로고
    • On ultrametricity, data coding, and computation
    • F. Murtagh, “On ultrametricity, data coding, and computation,” J. Classification 21, 167–184 (2004).
    • (2004) J. Classificatio , vol.21 , pp. 167-184
    • Murtagh, F.1
  • 7
    • 77951903530 scopus 로고    scopus 로고
    • Mumford dendrograms
    • P. E. Bradley, “Mumford dendrograms,” J. Classification 53, 393–404 (2010).
    • (2010) J. Classificatio , vol.53 , pp. 393-404
    • Bradley, P.E.1
  • 9
    • 55549116015 scopus 로고    scopus 로고
    • Hierarchical clustering of massive, high dimensional data sets by exploiting ultrametric embedding
    • F. Murtagh, G. Downs and P. Contreras, “Hierarchical clustering of massive, high dimensional data sets by exploiting ultrametric embedding,” SIAM J. Sci. Computing 30(2), 707–730 (2008).
    • (2008) SIAM J. Sci. Computin , vol.30 , Issue.2 , pp. 707-730
    • Murtagh, F.1    Downs, G.2    Contreras, P.3
  • 10
    • 0035561610 scopus 로고    scopus 로고
    • Segmentation of images in p-adic and Euclidean metrics
    • J. Benois-Pineau, A. Yu. Khrennikov and N. V. Kotovich, “Segmentation of images in p-adic and Euclidean metrics,” Dokl. Math. 64, 450–455 (2001).
    • (2001) Dokl. Math , vol.64 , pp. 450-455
    • Benois-Pineau, J.1    Khrennikov, A.Y.2    Kotovich, N.V.3
  • 12
    • 0035789317 scopus 로고    scopus 로고
    • Random projection in dimensionality reduction: applications to image and text data
    • ACM, New York, NY:
    • E. Bingham and H. Mannila, “Random projection in dimensionality reduction: applications to image and text data,” Proc. Seventh Int. Conf. on Knowledge Discovery and Data Mining, 245–250 (ACM, New York, NY, 2001).
    • (2001) Proc. Seventh Int. Conf. on Knowledge Discovery and Data Minin , pp. 245-250
    • Bingham, E.1    Mannila, H.2
  • 13
    • 0001883762 scopus 로고    scopus 로고
    • Experiments with random projection
    • Morgan Kaufmann Publ., San Francisco, CA:
    • S. Dasgupta, “Experiments with random projection,” Proc. 16th Conf. on Uncertainty in Artificial Intelligence, 143–151 (Morgan Kaufmann Publ., San Francisco, CA, 2000).
    • (2000) Proc. 16th Conf. on Uncertainty in Artificial Intelligenc , pp. 143-151
    • Dasgupta, S.1
  • 14
    • 38449115187 scopus 로고    scopus 로고
    • Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification
    • IEEE Comp. Society, Washington, DC:
    • S. Deegalla and H. Boström, “Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification,” ICMLA’ 06, Proc. 5th Int. Conf. on Machine Learning and Applications, 245–250 (IEEE Comp. Society, Washington, DC, 2006).
    • (2006) ICMLA’ 06, Proc. 5th Int. Conf. on Machine Learning and Application , pp. 245-250
    • Deegalla, S.1    Boström, H.2
  • 15
    • 84949855992 scopus 로고    scopus 로고
    • Random projection for high dimensional data clustering: a cluster ensemble approach
    • AAAI Press, Washington, DC:
    • Xiaoli Zhang Fern and C. Brodly, “Random projection for high dimensional data clustering: a cluster ensemble approach,” Proc. Twentieth Int. Conf. on Machine Learning, (AAAI Press, Washington, DC, 2007).
    • (2007) Proc. Twentieth Int. Conf. on Machine Learnin
    • Fern, X.Z.1    Brodly, C.2
  • 20
    • 0001654702 scopus 로고
    • Extensions of Lipschitz maps into a Hilbert space
    • W. B. Johnson and J. Lindenstrauss, “Extensions of Lipschitz maps into a Hilbert space,” Contemp. Math. 26, 189–20 (1984).
    • (1984) Contemp. Math , vol.26 , pp. 120-189
    • Johnson, W.B.1    Lindenstrauss, J.2
  • 21
    • 0037236821 scopus 로고    scopus 로고
    • An elementary proof of a theorem of Johnson and Lindenstrauss
    • JohnWiley & Sons, Inc., New York:
    • S. Dasgupta and A. Gupta, “An elementary proof of a theorem of Johnson and Lindenstrauss,” Random Struct. & Algorithms 22(1), 60–65, (JohnWiley & Sons, Inc., New York, 2003).
    • (2003) Random Struct. & Algorithm , pp. 60-65
    • Dasgupta, S.1    Gupta, A.2
  • 22
    • 38249031101 scopus 로고
    • The Johnson-Lindenstrauss lemma and the sphericity of some graphs
    • Acad. Press, Inc., Orlando, FL:
    • P. Frankl and H. Maehara, “The Johnson-Lindenstrauss lemma and the sphericity of some graphs,” J. Combin. Theory B 44(3), 355–362 (Acad. Press, Inc., Orlando, FL, 1988).
    • (1988) J. Combin. Theory , pp. 355-362
    • Frankl, P.1    Maehara, H.2
  • 26
    • 33244462202 scopus 로고    scopus 로고
    • Scalable partitioning and exploration of chemical spaces using geometric hashing
    • D. Dutta, R. Guha, P. C. Jurs and Ting Chen, “Scalable partitioning and exploration of chemical spaces using geometric hashing,” J. Chemical Inform. Modeling 46(1), 321–33 (Amer. Chemical Soc., 2006).
    • (2006) J. Chemical Inform. Modelin , vol.46 , Issue.1 , pp. 321-333
    • Dutta, D.1    Guha, R.2    Jurs, P.C.3    Chen, T.4
  • 27
    • 29344470029 scopus 로고    scopus 로고
    • Audio fingerprinting: nearest neighbor search in high dimensional binary spaces
    • Kluwer Acad. Publ., Hingham, MA:
    • M. L. Miller, M. Acevedo Rodriguez and I. J. Cox, “Audio fingerprinting: nearest neighbor search in high dimensional binary spaces,” J. VLSI Signal Proc. Systems 41(3), 285–291 (Kluwer Acad. Publ., Hingham, MA, 2005).
    • (2005) J. VLSI Signal Proc. System , pp. 285-291
    • Miller, M.L.1    Rodriguez, M.A.2    Cox, I.J.3
  • 28
    • 79952194271 scopus 로고    scopus 로고
    • Satellite image retrieval using low memory locality sensitive hashing in Euclidean space
    • R. Buaba, A. Homaifar, M. Gebril, E. Kihn and M. Zhizhin, “Satellite image retrieval using low memory locality sensitive hashing in Euclidean space,” Earth Sci. Informatics 4, 17–28 (2011).
    • (2011) Earth Sci. Informatic , vol.4 , pp. 17-28
    • Buaba, R.1    Homaifar, A.2    Gebril, M.3    Kihn, E.4    Zhizhin, M.5
  • 31
    • 0034504507 scopus 로고    scopus 로고
    • Indyk, “Stable distributions, pseudorandom generators, embeddings and data stream computation,” Found
    • Redondo Beach: CA
    • P. Indyk, “Stable distributions, pseudorandom generators, embeddings and data stream computation,” Found. Computer Science FOCS 2000, 189–197 (Redondo Beach, CA, 2000).
    • (2000) Computer Science FOCS 200 , vol.189-197
  • 32
    • 20744451888 scopus 로고    scopus 로고
    • Geometric representation of high dimension, low sample size data
    • P. Hall, J. S. Marron and A. Neeman, “Geometric representation of high dimension, low sample size data,” J. Royal Stat. Society B 67, 427–444 (2005).
    • (2005) J. Royal Stat. Society , vol.67 , pp. 427-444
    • Hall, P.1    Marron, J.S.2    Neeman, A.3
  • 35
    • 35949015913 scopus 로고
    • Ultrametricity for physicists
    • R. Rammal, G. Toulouse and M. A. Virasoro, “Ultrametricity for physicists,” Rev. Mod. Phys. 58(3), 765–788 (Amer. Phys. Soc., 1986).
    • (1986) Rev. Mod. Phys , vol.58 , Issue.3 , pp. 765-788
    • Rammal, R.1    Toulouse, G.2    Virasoro, M.A.3
  • 36
    • 84967024740 scopus 로고    scopus 로고
    • Introduction to Lattices and Order(Cambridge Univ
    • B. A. Davey and H. A. Priestley, Introduction to Lattices and Order(Cambridge Univ. Press, 2002).
    • (2002) Pres
    • Davey, B.A.1    Priestley, H.A.2
  • 37
    • 77951912084 scopus 로고    scopus 로고
    • Generalized distance functions in the theory of computation
    • A. K. Seda and P. Hitzler, “Generalized distance functions in the theory of computation,” Computer J. 53, 443–464 (2010).
    • (2010) Computer J , vol.53 , pp. 443-464
    • Seda, A.K.1    Hitzler, P.2
  • 38
    • 0040240479 scopus 로고
    • An order theoretic model for cluster analysis
    • M. F. Janowitz, “An order theoretic model for cluster analysis,” SIAM J. Appl. Math. 34, 55–72 (1978).
    • (1978) SIAM J. Appl. Math , vol.34 , pp. 55-72
    • Janowitz, M.F.1
  • 40
    • 0020765701 scopus 로고
    • Expected time complexity results for hierarchic clustering algorithms that use cluster centers
    • F. Murtagh, “Expected time complexity results for hierarchic clustering algorithms that use cluster centers,” Information Proc. Lett. 16, 237–241 (1983).
    • (1983) Information Proc. Lett , vol.16 , pp. 237-241
    • Murtagh, F.1
  • 42
    • 0036039291 scopus 로고    scopus 로고
    • A new cell-based clustering method for large, high-dimensional data in data mining applications
    • ACM, New York:
    • Jae-Woo Chang and Du-Seok Jin, “A new cell-based clustering method for large, high-dimensional data in data mining applications,” SAC’ 02, Proc. 2002 ACM Symposium on Applied Computing, 503–507 (ACM, New York, 2002).
    • (2002) SAC’ 02, Proc. 2002 ACM Symposium on Applied Computin , pp. 503-507
    • Chang, J.-W.1    Jin, D.-S.2
  • 44
    • 14344255219 scopus 로고    scopus 로고
    • Statistical grid-based clustering over data streams
    • ACM, New York:
    • Nam Hun Park and Won Suk Lee, “Statistical grid-based clustering over data streams,” SIGMOD Record 33(1), 32–37 (ACM, New York, 2004).
    • (2004) SIGMOD Recor , pp. 32-37
    • Park, N.H.1    Lee, W.S.2
  • 45
    • 67949110882 scopus 로고    scopus 로고
    • Clustering (IEEE Comp. Soc
    • Rui Xu and D. C. Wunsch, Clustering (IEEE Comp. Soc. Press, 2008).
    • (2008) Pres
    • Rui, X.1    Wunsch, D.C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.