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  • $BB?JQNL2r@O(B
    • $BEDCfK-(B, $BOFK\OB>;(B, $BB?JQNLE}7W2r@OK!(B, $B8=Be?t3X $B2s5"J,@O!$
    • $B%A%c!<%N%U$N4i%0%i%U(B (Chernoff faces)
      • Herman Chernoff, The use of faces to represent points in k-dimensional space graphically, Journal of the American Statistical Association, Vol. 68, pp. 361-368, 1973.
      • Clifford A. Pickover, Computers, Pattern, Chaos, and Beauty, St. Martin's Press, 1990. ($B9b66;~;TO:(B, $BFbF#><;0(B $BLu(B, $B%3%s%T%e!<%?!&%+%*%9!&%U%i%/%?%k(B $B!](B $B8+$($J$$@$3&$N%0%i%U%#%C%/%9(B, $BGrMH Appendix B$B$K(Bpseudocode$B$,$"$k!%(B
      • R -- $B%A%c!<%N%U$N4i%0%i%U(B by $B@DLZHK?-(B
    • Leonard Kaufman, Peter J. Rousseeuw, Finding Groups in Data, Wiley-Interscience, 1990.
      silhouette coefficient, agglomerative coefficient, divisive coefficient, etc. (cf. inconsistency coefficient)
  • $BE}7W(B
    • S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Statist., Vol. 22, pp. 79-86, 1951.
    • $B8!Dj(B
      • $B&V(B2$B8!Dj(B
      • $B%U%#%C%7%c!<$ND>@\3NN(8!Dj(B (Fisher's exact probability test)
      • $B%^%s!&%[%$%C%H%K!<$N(BU$B8!Dj(B (Mann-Whitney's U test)
      • $B%&%#%k%3%/%=%s$NId9f=g0L8!Dj(B (Wilcoxon test)
      • $B%/%i%9%+%k!&%o!<%j%98!Dj(B (Kruskal-Wallis test)
      • Kolgomorov-Smirnov (KS) test
        ....
    • $B>r7o$KE,9g$7$?E}7W2r@O
    • $B%D!<%k(B
  • BLI (Bounded Locality Interval)
    • A. W. Madison, Characteristics of Program Localities, University Microfilms International, Ann Arbor, Mich., 1982.
      $B;2>H$N6I=j@-$rB,$k$?$a$N%a%H%j%C%/!%(B Henderson $B$H(B Card $B$,%f!<%6$K$h$k%&%#%s%I%&MxMQ$NMM;R$r2r@O$9$k$?$a$K;HMQ!%(B
  • $B;~7ONs2r@O(B
    • FFT (Fast Fourier Transform)
    • Rakesh Agrawal, Ramakrishnan Srikant, Mining Sequential Patterns, Eleventh International Conference on Data Engineering, pp. 3-14, 1995.
    • DFA (Detrended Fluctuation Analysis)
    • Segmentation algorithm
      Pedro Bernaola-Galván, Plamen Ch. Ivanov, Luís A. Nunes Amaral, H. Eugene Stanley, Scale Invariance in the Nonstationarity of Human Heart Rate, Physical Review Letters, Vol. 87, No. 16, 168105, 2001.
  • $B5!3#3X=,(B
    • $BJ,N`4o(B
      • naive Bayes
      • k-Nearest Neighbor
        • T. Cover and P. Hart, Nearest Neighbor Pattern Classification, IEEE Trans. on Information Theory, pp. 21-27, 1967.
      • $B7hDjLZ(B
        • ID3
        • C4.5
      • $B:GBg%(%s%H%m%T!
      • SVM (Support Vector Machine)
        • Kernel Machines
        • $B9b?\=_9((B, Support Vector Machine$B$K$h$kJ,N`(B, bit$BJL:}(B: $BH/8+2J3X$H%G!<%?%^%$%K%s%0(B, $B6&N)=PHG(B, 2000$B!%(B
        • $BA0ED1Q:n(B, $BDK2w!*%5%]!<%H%Y%/%H%k%^%7%s(B, $B>pJs=hM}(B, Vol. 42, No. 7, 2001.
        • Thorsten Joachims, Making large-Scale SVM Learning Practical, in Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges and A. Smola (eds.), MIT Press, 1999.
          SVM-Light.
        • LIBSVM by Chih-Chung Chang and Chih-Jen Lin.
    • LAD (Logical Analysis of Data)
    • Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore, Automating the Construction of Internet Portals with Machine Learning, Information Retrieval, Vol. 3, No. 2, pp. 127-163, 2000.
    • Rainbow
      A program that performs statistical text classification. Based on the Bow library.
    • WEBSOM
    • Weka Machine Learning Project at School of Computing and Mathematical Sciences, Univ. of Waikato
      • Weka: Machine Learning Software in Java
        Classification, regression, clustering, association rules, visualization.
      • Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 1999.
        Weka$B$r;H$C$?%G!<%?%^%$%K%s%0$N$d$jJ}!%(B
    • $B1JED>;L@(B, $BJ?Gn=g(B, $B%F%-%9%HJ,N`(B $B!]3X=,M}O@$N!V8+K\;T!W!](B, $B>pJs=hM}(B, Vol 42, No. 1, 2001.
      $BJ8=qJ,N`!a(B $BJ8=q$NAG@-A*Br(B($BIQEY(B, $B>pJsMxF@(B, $B&VFs>h8!Dj(B, LSI)$B!\(B $BJ,N`4o(B($B7hDjLZ(B, k-$B:G6aNYK!(B, $B:GBg%(%s%H%m%T!e$7$F$$$/!%(B
    • Yiming Yang, Jan O. Pedersen, A comparative study on feature selection in text categorization, in Proceedings of the 14th International Conference on Machine Learning (ICML-97), pp. 412-420, 1997.
      $B5!3#3X=,$K$h$kJ8=qJ,N`$K$*$1$kFCD'8lA*Br$NBeI=E*$J(B5$B$D$NJ}K!$G$"$k(B document frequency (DF), information gain (IG), mutual information (MI), $B&V(B2 test (CHI), term strength (TS)$B$rHf3S!%(B IG$B$H(BCHI$B$,:G$b8z2LE*!%(BIG, CHI, DF$B$N4V$K$O6/$$Aj4X$,$"$k$N$G!$(B $B7W;;%3%9%H$r2<$2$?$$$H$-$K$O(BDF$B$r;H$&$H$h$$!%(B
  • ICA (Independent Component Analysis)
  • $B%G!<%?!&%^%$%K%s%0(B
    • Apriori
    • interestingness
      • Avi Silberschatz, Alexander Tuzhilin, What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Trans. On Knowledge And Data Engineering, Vol. 8, No. 6, pp. 970-974, 1996.
      • Sigal Sahar, Interestingness via what is not interesting, in Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 332-336, 1999.
    • $B?@V:IR90(B, $B%G!<%?%^%$%K%s%0J,Ln$N%/%i%9%?%j%s%0, $B?M9)CNG=3X2q;o(B, Vol. 18, No. 1, 2003.
    • $BFC=8(B $B:G?7!*%G!<%?%^%$%K%s%0(B, $B>pJs=hM}(B, Vol. 46, No. 1, 2005.
      • $BM-B$B%G!<%?%9%H%j!<%`$N$?$a$N%^%$%K%s%05;=Q(B
      • $BNkLZ1QG7?J(B, $B%G!<%?%9%+%C%7%s%0(B
      • $BOIHxN4(B, $B%0%i%U%Y!<%9%G!<%?%^%$%K%s%0$N4pAC$H8=>u(B
      • $B$B%+!<%M%kK!$K$h$k9=B$%G!<%?%^%$%K%s%0(B
      • $B;3@>7r;J(B, $BC]Fb=c0l(B, $B4];3M4;R(B, $BE}7WE*0[>o8!=P(B3$B
      • $B9)F#Bs(B, $B?7J]?N(B, $B<+A38@8l$K$*$1$k%^%$%K%s%05;=Q$N1~MQ(B
      • $BK-ED@5;K(B, $B4nO"@nM%(B, $BBg5,LO(BWeb$B%"!<%+%$%V$+$i$N%G!<%?%^%$%K%s%0(B
    • $BFC=8(B $B%]%9%H%2%N%`;~Be$K9b$^$k%P%$%*<+A38@8l=hM}$X$N4|BT!'(B $B%P%$%*<+A38@8l=hM}:G?7;v>p(B, $B>pJs=hM}(B, Vol. 46, No. 2, 2005.
      • $BCfB<7K;R(B, $B8l$k2J3X$X8~$1$F(B $B!=(B $B%G!<%?!&CN<1!&@8L?8=>]$r$D$J$0(B $B!=(B, pp. 107-111.
    • Marti A. Hearst, Untangling Text Data Mining, in the 37th Annual Meeting of the Association for Computational Linguistics, 1999.
  • $B%0%i%U(B
    • D. Cvetkovic;, M. Doob, H. Sachs, Spectra of Graphs Academic Press, 1979.
    • Spectral clustering
    • $BEDCf1I0l(B, $B9=B$$r$b$D$b$N$N5wN%$HN`;wEY(B, $B>p=h3X2q;o(B, Vol. 31, No. 9, pp. , 1990.
    • $B>e86N4J?(B, $B%0%i%U%/%i%9$H%"%k%4%j%:%`(B, $BEE;R>pJsDL?.3X2q;o(B, Vol. 88, No. 2, pp. 118-122, 2005.
    • $B2r@O(B/$B2D;k2=(B
      • T. Kamada and S. Kawai, An algorithm for drawing general undirected graphs, Information Processing Letters, Vol. 31, No. 1, pp. 7-15, 1989.
      • Thomas M. J. Fruchterman, Edward M. Reingold, Graph Drawing by Force-directed Placement, Software - Practice and Experience, Vol. 21, No. 11, pp. 1129-1164, 1991.
      • Otter
      • Pajek at Institute for Mathematics, Physics and Mechanics, University of Ljubljana, Slovenia.
        • Vladimir Batagelj, Andrej Mrvar, Matjaž Zaveršnik, Partitioning Approach to Visualization of Large Networks, in Proceedings of the 7th International Symposium on Graph Drawing (GD'99), LNCS 1731, pp. 90-97, Springer-Verlag, 1999.
        • Vladimir Batagelj, Andrej Mrvar, Pajek Program for Large Network Analysis, in M. Jünger, P. Mutzel (Eds.), Graph Drawing Software, pp. 77-103, Springer, 2003.
      • Bill Cheswick, Hal Burch, and Steve Branigan, Mapping and Visualizing the Internet, in Proceedings of 2000 USENIX Annual Technical Conference, pp. 1-12, 2000.
      • JUNG (Java Universal Network/Graph Framework) at School of Information & Computer Science, University of California, Irvine.
        • Scott White, Padhraic Smyth, Algorithms for Discovering Relative Importance In Graphs, in Proceedings of Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
      • LGL (Large Graph Layout)
        Opte Project$B$G(B Internet$B$N(Bmap$B$r:n$k(B$B$?$a$K;H$o$l$F$$$k!%(B
      • David Soen-Mun Chan, Khim Shiong Chua, Christopher Leckie, Ajeet Parhar, Visualisation of power-law network topologies, in Proceedings of the 11th IEEE International Conference on Networks (ICON2003), pp. 69-74, 2003.
        Power-Law network$B$r8zN(NI$/IA$/%"%k%4%j%:%`(BODL$B$NDs0F!%(B $B%l%.%e%i!<%M%C%H%o!<%/$H(BBGP$B$N(BAS$B%^%C%W$rJ#?t$N%"%k%4%j%:%`(B (ODL, Fruchterman-Reingold, Walshaw, ISOM, Kamada-Kawai) $B$GIA2h$7!$BP>N@-$H%/%i%9%?@-$,E,@Z$KI=8=$5$l$F$$$k$+$rHf3S!%(B $BBP>N@-$NE@$G$O(B ODL, Fruchterman-Reingold, Kamada-Kawai$B$,$h$/!$(B $B%/%i%9%?@-$NE@$G$O(B ODL, Fruchterman-Reingold $B$,$h$$!%(B $BAm9g$9$k$H!$8+1I$($NE@$G$O(B Fruchterman-Reingold $B$,0lHV!%(B
      • Andreas Noack An Energy Model for Visual Graph Clustering, Graph Drawing 2003, pp. 425-436, 2003.
        LinLog$B%"%k%4%j%:%`!%(B Fruchterman-Reingold $B$J$I$h$j$b%/%i%9%?$I$&$7$NJ,N%@-$r9b$a$FG[CV$G$-$k!%(B
      • WebGraph
      • aiSee
      • tulip
      • $B;3EDIp;N(B, $B@FF#OB8J(B, $B>eED=$8y(B, $B%/%m%9%(%s%H%m%T!<:G>.2=$K4p$E$/%M%C%H%o!<%/%G!<%?$NKd$a9~$_(B, $B>pJs=hM}3X2qO@J8;o(B, Vol.44, No.9, pp. 2401-2408, 2003.
      • Graph Drawing category in Google Directory
    • complex network
    • social network
  • $B7W;;4v2?(B
  • $B%0%l%$%3!<%I(B
    • $BN)LZ=( 2$B?JK!$G$O(B 0, 1, 10, 11 $B$HI=5-$5$l$k?t$,!$(B $B%0%l%$%3!<%I$G$O(B 0, 1, 11, 10 $B$H$J$k!%(B $BCM$,(B1$B$D0[$J$k?t$I$&$7$GI=5-$,(B1$BJ8;z$7$+JQ2=$7$J$$!%(B
CSCW
  • $BB?MM@-$H7k$SIU$-(B
    • Don R. Swanson, Undiscovered public knowledge, Library Quarterly, 56 pp. 103-118, 1986.
      2$B$D$NCN<1$,7k$SIU$1$P?7$7$$H/8+$r@8$`2DG=@-$,$"$k$K$b$+$+$o$i$:!$(B $B$=$l$>$l8DJL$N%3%_%e%K%F%#!J@lLgNN0h!K$NCf$G$N$_$d$j$H$j$5$l!$(B $BN>J}$NCN<1$r;}$D?M$,$$$J$$$H$$$&>u67!%(B
    • Don R. Swanson, Fish oil, Raynaud's syndrome and undiscovered public knowlege, Perspectives in Biology and Medicine, 30(1), pp. 7-18, 1986.
      undiscovered public knowledge$B$NNc$H$7$F!$(B $B%l%$%N!<>I8u72$H5{L}!JM=KI$K8z2L$,$"$k!K$N%1!<%9$r>R2p!%(B
    • B. Huberman, T. Hogg, The Behavior of Computational Ecologies, in Huberman, B., ed., The Ecology of Computation, pp. 77-115, North-Holland, 1988.
      $B%0%k!<%W$K$h$k2J3X8&5f$J$I$G$O!$(B $B8D!9?M$N$b$N$N8+J}$NB?MM@-$,H/8+$N3NN($r8~>e$5$;$k!%(B
    • Lee Fleming, Perfecting Cross-Pollination, Harvard Business Review, Vol. 82, No. 9, pp. 22-24, 2004.
      $BCNE*@8;:3hF0$N$?$a$N%A!<%`$K$*$1$k%a%s%P$NB?MM@-$N8z2L!%(B $B%a%s%P$N@lLgJ,Ln$,N`;w$7$F$$$k>l9g$HB?MM@-$KIY$s$G$$$k>l9g$H$r(B $BF@$i$l$?@.2L$N(B($B%$%N%Y!<%7%g%s$H$7$F$N(B)$B2ACM$GHf3S$9$k$H!$(B $BJ?6Q$G$OA0
  • GroupLens at University of Minnesota
    • Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom and John Riedl, GroupLens: An Open Architecture for Collaborative Filtering of Netnews, in Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175-186, 1994.
  • Social filtering
    • Upendra Shardanand and Pattie Maes, Social Information Filtering: Algorithms for Automating ``Word of Mouth'', Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 210-217, 1995.
    • Henry Kautz, Bart Selman, and Mehul Shah, Referral Web: Combining Social Networks and Collaborative Filtering, Communications of the ACM, Vol. 40, No. 3, pp. 63-65, 1997.
  • Recommender Systems
    • Paul Resnick and Hal R. Varian, Recommender Systems, CACM Volume 40 , No. 3, pp. 56-58, 1997.
    • Pattie Maes, Robert H. Guttman and Alexandros G. Moukas, Agents that Buy and Sell: Transforming Commerce as we Know It, Communication of the ACM, Vol.42, No.3, pp. 81-91, 1999.
      Firefly$B$J$I$,Hf3SI>2A$5$l$F$$$k!%(B
  • $B%M%C%H%o!<%/%3%_%e%K%F%#(B
    • $BG_LZ=(M:(B, $B%M%C%H%o!<%/%3%_%e%K%F%#7A@.;Y1g5;=Q(B, $B?M9)CNG=3X2q;o(B Vol. 14, No. 6, pp. 943-950, 1999.
      $BL@<(E*%3%_%e%K%F%#(B, $B0E<(E*%3%_%e%K%F%#!%(B LOUIS$B!%(B
  • $B%=!<%7%c%k%$%s%?%i%/%7%g%s(B
    • $B?"ED0lGn(B, $B%3%_%e%K%1!<%7%g%s$r<4$H$7$??7$7$$%7%9%F%`CN$N$"$jJ}(B, $B!VFC=8!'%=!<%7%c%k%$%s%?%i%/%7%g%s!W(B, $B>p=h3X2q;o(B, Vol. 40, No. 6, 1999$B!%(B
      $B%0%k!<%W$K$h$kC1=c$JLdBj2r7h:n6H$N%Q%U%)!<%^%s%9$O!$(B $BJ?6Q$r>e2s$C$F$b:GNI$N8D?M$K$O5Z$P$J$$!%(B $BAOH/@-$,4|BT$5$l$k$h$&$J>lLL$G$3$=6(D4:n6H$N0U5A$,$"$k!%(B
  • CMC (Computer Mediated Communication)
    • $B>>B2$B$A$c$s$M$k$,@9$j>e$,$k%@%$%J%_%:%`(B, $B>pJs=hM}3X2qO@J8;o(B, Vol.45, No.3, pp. 1053-1061, 2004.
    • Blog
Human Factor
  • Weber-Fechner($B%&%'!<%P!
    $B463PNL(B/$B?4M}NL$O;I7c$NBP?t$KHfNc!%(B
  • John R. Anderson ad L. J. Schooler, Reflections of the Environment in Memory, Psychological Science, 2(6), pp. 396-408, 1991.
    $B?M4V$N5-21$NFC@-$O%Y%-K!B'$K=>$&!%(B $B$^$?!$$"$k
  • J. Shrager and T. Hogg and B. A. Huberman, A Graph-Dynamic Model of the Power Law of Practice and the Problem-Solving Fan-Effect, Science, Vol. 242, No. 4877, pp. 414-416, 1998.
    $B=,=O6J@~$O%Y%-4X?t!%(B
  • $BIT@53N$J5-21$KBP$9$k9=@.E*=hM}(B
    • E. F. Loftus, D. G. Miller, and H. J. Burns, Semantic integration of verbal information into a visual memory, Journal of Experimental Psychology: Human Learning and Memory, 4, pp. 19-31, 1978.
    • $BB<>e@2H~(B, $BF|K\8l$N2N;l$ND94|5-21$K$*$1$k9=@.E*=hM}(B $B!]!V7V$N8w!W$H!V6D$2$PB:$7!W$rBj:`$H$7$F(B, The Second International Conference on Cognitive Science and The 16th Annual Meeting of the Japanese Cognitive Science Society Joint Conference (ICCS/JCSS99), pp.842-845, Tokyo, Japan, 1999.
  • Fan Effect
    • J. R. Anderson, Retrieval of propositional information from long-term memory, Cognitive Psychology, 6, 451-474, 1974.
    • J. R. Anderson, A spreading activation theory of memory, Journal of Verbal Learning and Verbal Behavior, Vol. 22, pp. 261-295, 1983.
    • $B$"$k35G0$K$D$$$FB?$/3X$V(B($B5-21$9$k(B)$B$[$I!$$=$N35G0$K4X$9$k;vJA$r(B $B5-21$+$i0z$-=P$9$N$K;~4V$,$+$+$k$H$$$&8=>]!%(B Anderson$B$O!$(B $B$3$N8=>]$r@bL@$9$k(BACT(Adaptive Control of Thought)$B$H$$$&%b%G%k$rDs>'!%(B $B4XO"$9$k5-21(B($B$5$l$F$$$k;vJA(B)$B$,8_$$$K7Q$j%M%C%H%o!<%/$r7A@.$9$k!%(B $B5-21$O;H$o$l$k$3$H$K3h@-2=$5$l$k!%$=$N3h@-$OEAHB$5$l7Q$j$,6/2=$5$l$k!%(B $BB?$/$N5-21$,7Q$C$F$$$k$H3h@-$,J,;6$7$F$7$^$$!$(B $B$=$N7k2L(Bfan effect$B$,5/$-$k!%(B
  • Theories and Hypotheses Related to Integrated Cognitive Architectures
  • D. Austin Henderson, JR., and Stuart K. Card, Rooms: The Use of Multiple Virtual Workspace to Reduce Space Contention in Window-Based Graphical User Interface, ACM Transaction on Graphics, Vol.5, No.3, July 1986, pp. 211-243.
    Window Thrashing. $B%&%#%s%I%&%7%9%F%`$G$N%&%#%s%I%&$N;H$o$lJ}(B ($B;HMQIQEY!$;HMQ%&%#%s%I%&?t(B)$B$N2r@O!%(B $B%&%#%s%I%&$NMxMQ$K6I=j@-$,$_$i$l$k!%(B
  • $BO"8@$N:x8m(B (Conjunction Fallacy)
    • Amos Tversky and Daniel Kahneman, Judgments of and by representatitveness, in ``Judgment under uncertainty: Heuristics and biases,'' Daniel Kahneman, Paul Slovic and Amos Tversky (Eds.), Cambridge University Press, Cambridge, UK, pp. 84-98, 1982.
      $B!V%j%s%@$O(B31$B:M$NFH?H$N=w@-$G3hH/$GHs>o$KAoL@$G$"$k!%Bg3X$N@lLg$OE/3X$@$C$?!%(B $B3X@8$N;~$KH`=w$O:9JLLdBj$d$l$,Ev$F$O$^$k3NN($O$I$l$/$i$$$+$rH=CG$5$;$k$H$$$&2]Bj$r=P$9!%(B
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      $B$3$N$H$-!"(B(h) = (c) AND (f) $B$J$N$K$b$+$+$o$i$:!"(B $B0lHL$K?M$O!"(B (c) > (h) > (f) $B$N$h$&$K3NN($rH=CG$7$F$7$^$&!#(B
  • $BFo8+9'(B, $BN`;w@-$H6a@\@-(B $B!](B $B?M4V$NG'CN$NFCD'$K$D$$$F(B, $B?M9)CNG=3X2q;o(B, Vol. 17, No. 1, 2002.
  • $B%"%U%)!<%@%s%9(B (J.J.Gibson)
    • $B4D6-$,9TF0$rM6H/$9$k!%(B $B9T0Y$KH<$C$F4D6-$+$i0z$-=P$5$l$k9T0Y$N2DG=@-!%(B $BCN3P$H$O9T0Y$r0O$`>u67$N@8@.$G$"$j!$9T0Y$H$O@Z$jN%$;$J$$!%(B
  • $B>>86?N(B, $B?HBN@-$N0UL#$9$k$H$3$m(B, bit$BJL:}(B: $B?HBN@-$H%3%s%T%e!<%?(B, $B6&N)=PHG(B, 2000$B!%(B
    $B>pJs=hM}$NBP>]$rE,Ev$K69$a$k!VpJs$NItJ,@-(B) $B$,?M4V$NCNG=$r;Y$($F$*$j!$$3$l$K$h$j?M4V$O%U%l!<%`LdBj$r2sHr$7$F$$$k!%(B $B$=$7$F!$>pJs$NItJ,@-$O?HBN@-$K5/0x$9$k!%(B
  • $BG>$N>pJs=hM}(B
    • $B;~4V%3!<%G%#%s%0(B (temporal coding)
      • W. J. Freeman $B$K$h$k%&%5%.$NSL5e$K$*$1$k3hF0%Q%?%s$N2r@O(B
        ($B$"$k;~E@$G$N6u4VE*(B)$B3hF0%Q%?%s$O(B $B30It>pJs(B($B=-$$(B)$B$r$=$N$^$^%3!<%I2=$7$?$b$N$G$O$J$/!$(B $B;I7cDs<($NMzNr$K0MB8$7$?$b$N$G$"$k!%(B
    • $B%+%*%9E*JWNr(B ($BDEED0lO:(B)
User Interface$B!$;k3P2=(B
  • George W. Furnas, Effective View Navigation, Proceedings of ACM CHI 97 Conference on Human Factors in Computing Systems, Vol. 1, pp.367-374, 1997.
    Efficient View Traversability (EVT).
    View Navigability (VN); residue & scent for improving VN.
    Effective View Navigability (EVN) = EVT + VN.
  • Zooming Interface
    • Pad++
    • Jazz
      A successor to Pad++ implemented in Java.
    • HishiMochi
      $BK-ED@5;K(B, $BpJs8!:w%$%s%?%U%'!<%9(B, bit$BJL:}(B: $BH/8+2J3X$H%G!<%?%^%$%K%s%0(B, 2000.
  • A. Spoerri, Visual Tools for Information Retrieval, Proc.of 1993 IEEE Symposium on Visual Languages (VL'93), pp. 160-168, 1993
    InfoCrystal$B!%(B Venn$B?^$r1~MQ$7$?>pJs8!:w;Y1g!%(B
  • WEBSOM
  • $B[kLn7{9n(B, $B;3ED@?Fs(B, $B%^%k%A(B Web $B%m%\%C%H$K$h$k%f!<%6$N6=L#$rH?1G$7$?>pJs<}=8(B, $BEE;R>pJsDL?.3X2qO@J8;o(B, Vol. J83-D-I, No.7, pp. 780-788, July 2000.
  • Robert R. Korfhage, To see or not to see -- is that the query?, Conference on Research and Development in Information Retrieval, pp. 134-141. ACM, ACM Press, October 1991.
    VIVE (search by direct keyword manipulation)$B!%(B
  • $B4\B<=c0l!$(B DocSpace:$BJ88%6u4V$N%$%s%?%i%/%F%#%V;k3P2=(B, WISS'96, 1996.
    $B$P$M%b%G%k!%(B
  • $BEOn47CB@(B, $B0BBMemorium: $BD/$a$k%$%s%?%U%'!<%9$NDs0F$H$=$N;n:n(B, $BBh(B10$B2s(B $B%$%s%?%i%/%F%#%V%7%9%F%`$H%=%U%H%&%'%"$K4X$9$k%o!<%/%7%g%C%W(B(WISS2002)$BO@J8=8(B, pp.99-104, 2002.
    $BM-8BWFM$9$k$H!$$=$l$i$N%?%$%H%k$r$b$H$K(BAND$B8!:w$,9T$o$l?7$7$$%+!<%I$,@8@.$5$l$k!%(B
  • $B%0%i%U9=B$$N2D;k2=(B
  • User Interface Research Group at Xerox Parc
    • Historic Perspective on Visualizing the Interactions of Web Ecologies
    • WebBook and WebForager
      ....
  • Information Visualization at PNNL (Pacific Northwest National Laboratory)
  • $BA}0f=SG7(B, $B;k3P2=4XO"J88%(B$B!%(B
  • Rika Furuhata, Issei Fujishiro, Kana Maekawa, Yumi Yamashita, Applications of Information Visualizaitons (MIKY database: Information Visualization and Visualization Techniques)
$B
  • Social network
    • Stanley Wasserman, Katherine Faust, Social Network Analysis: Methods and Applications, Cambridge Univ. Press, 1994.
    • $BJ?>>oh(B ($BJTCx(B), $B, $BJ!B<=PHG(B, 1990.
      $BM'?M!?8r:]!??FB2$N%M%C%H%o!<%/!$@*NO!$%/%j!<%/!$E@Cf?4@-!%(B $BF1N`;X8~(B(inbreeding)$B!%(B
    • Small World
      • Stanley Milgram, Small World Problem, Psychology Today, 1, pp. 60-67.
      • P. S. Dodds, R. Muhamad, D. J. Watts, An experimental study of search in global social networks, Science, 301, pp. 827-829, 2003.
    • $B
      • Mark S. Granovetter, The strength of weak ties, American Journal of Sociology, Vol. 68, pp. 1360-1380, 1973.
        $B?7$7$$>pJs$OK\
      • Mark S. Granovetter, Getting a Job, University of Chicago Press, 1974. ($BK.Lu(B: $BE>?&(B, $B%_%M%k%t%!=qK<(B, 1998.)
      • $BEOJU?<(B, $BE>?&(B $B!]E>?&7k2L$K5Z$\$9%M%C%H%o!<%/$N8z2L!](B, $BO@(B, Vol. 42, No. 1, pp. 2-16, 1991.
        $BF|K\$NE>?&>u67$G$O$`$7$m6/$$I3BS$,;H$o$l$F$$$k!%(B
      • $B0BED@c(B, $B, $B?7MK Granovetter$B$N5DO@$G$O!$(Bweakness$B$h$j(Bbridge$B$N35G0$,=EMW!%(B bridge$B$H$O!$$=$l$,=|$+$l$F$7$^$&$H$^$H$^$j$,2u$l$F$7$^$&(B (1$B$D$@$C$?%0%i%U$,J#?t$N%5%V%0%i%U$KJ,$+$l$F$7$^$&(B)$B$h$&$JI3BS!%(B
    • $BCf?4@-(B
      • Linton C. Freeman, Centrality in Social Networks: Conceptual Clarication, Social Networks, 1, pp. 215-239, 1979.
        Betweenness($BG^2p@-(B), closeness.
    • complex network
  • $BLnEg5WM:(B, $B%G!<%?%Y!<%9$H$7$F$N(BWWW$B!$%G!<%?%Y!<%9$H$7$F$N, Computer Today, No. 84, pp. 60-67, 1998$B!%(B
  • $B1=!$N.9T(B
    • $B0KF#=_;R(B, $B=w;R9b@8$N%/%A%3%_$N$D$/$j$+$?(B, $B>pJs=hM}3X2q8&5f2qJs9p(B, 1995-IM-25-6, pp. 41-47 , 1995.
      $B%^%9%a%G%#%"$K$h$k>pJsEAHB$OAa$$$,:,$E$+$J$$(B($B%V!<%`$,Nd$a$k$N$bAa$$(B)$B!%(B $B>pJs$O$=$N$^$^J|$C$F$*$/$HIe$k!%(B $BL%NO$rJ];}$9$k$K$OJQ2=$5$;$k!J0i$F$k"b@83h$KL)Ce$5$;$k!KI,MW$,$"$k!%(B $B>.$5$J%3%_%e%K%F%#$K$O>pJs$r0i$F$kNO$,$"$k!%(B
    • $B@n>eA1O:(B, $B$o$5$,Av$k!=>pJsEAGE$N, $B%;%l%/%7%g%s $B%^%9!&%3%_%e%K%1!<%7%g%s$h$j%Q!<%=%J%k!&%3%_%e%K%1!<%7%g%s$NJ}$,(B $B%K%e!<%9$r$h$/EAGE$9$k$3$H$r