Datenbanken: Gruppe 2 Freitags 14–16 Uhr Matrikel-Nr 122054 128853 129564 133207 133270 133274 133450 133564 133592 133593 133614 133625 133824 133955 138163 140223 160981 163502 163508 2241962 2 …

36-KomDat-18.vp

801 7,9 5,2 davon: Westdeutschland (inkl. Berlin) 18.813 20.371 1.558 8,3 5,5 Ostdeutschland 3.757 3.975 218 5,8 3,1 Oberste Bundesjugendbehörde 223 248 25 11,2 8,4 Einnahmen insgesamt 2.252 2.342 89 4 [...] 5,4 12,4 7,0 2-Jährige 16,7 29,7 13,0 U3 insg. 8,0 14,6 6,6 Ostdeutschland (ohne Berlin) unter 1-Jährige 6,0 5,0 -1,0 1-Jährige 40,4 56,7 16,3 2-Jährige 72,6 77,8 5,2 U3 insg. 39,7 46,0 6,2 Tab. 1: Quote [...] 21-Jährigen Maßnahmen Teilneh- mende Maßnahmen2 Teilneh- mende3 1996 90.035 3.264.394 134 49 2000 82.055 2.895.852 117 41 2004 74.025 2.755.349 106 40 2008 73.325 2.583.148 113 40 Tab. 1: Öffentlich geförderte …

InformationsExtraktion.dvi

r de n en ts pr ec he n- de n T ex t is t. P G -4 0 2 In fo rm a ti o n se x tr a k ti o n C h ri st ia n H ü p p e < 0 . 2 1 . 3 . 9 5 . 1 4 . 1 2 . 1 1 . e d 4 7 + @ a n d r e w . c m u . e d u . 0 > [...] auditorium cmu computerized concrete construction 2 1 2 1 1 ... ... ... ... 4 n n/ do c 3 n A bb ild un g 4: D ie in ve rs e do cu m en t fr eq ue nc y 7 .2 M a rk -U p V ie w B ei m M ar k- U p V ie w w [...] 2001 Betreuer: Prof. Dr. Katharina Morik Stefan Haustein Inhaltsverzeichnis 1 Einleitung 3 2 Dokumentensuche 3 2.1 Information Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 Inf …

Partitioning Around Medoids (k-Medoids) – Lecture Notes

Algorithm: Partitioning Around Medoids /2 PAM SWAP , to improve the clustering: Per iteration: naïve approach: \(O(k^2(n-k)^2)\) ; PAM: \(O(k (n-k)^2)\) ; FastPAM: \(O(n^2)\) [ ScRo19 ; ScRo21 ] The authors [...] \textup{dist}(x_j, m_i)\) , but we can compute the change easier. This needs \(O(n^2 k)\) time, and is best implemented with \(O(n^2)\) memory. We could use this to seed \(k\) -means, too. But it is usually slower [...] between (1) optimizing the cluster assignment, and (2) choosing the best medoids can update all medoids in each iteration complexity: \(O(k\cdot n + n^2)\) per iteration, comparable to FastPAM but produces …

Partitioning Around Medoids (k-Medoids) – Lecture Notes

Algorithm: Partitioning Around Medoids /2 PAM SWAP , to improve the clustering: Per iteration: naïve approach: \(O(k^2(n-k)^2)\) ; PAM: \(O(k (n-k)^2)\) ; FastPAM: \(O(n^2)\) [ ScRo19 ; ScRo21 ] The authors [...] \textup{dist}(x_j, m_i)\) , but we can compute the change easier. This needs \(O(n^2 k)\) time, and is best implemented with \(O(n^2)\) memory. We could use this to seed \(k\) -means, too. But it is usually slower [...] between (1) optimizing the cluster assignment, and (2) choosing the best medoids can update all medoids in each iteration complexity: \(O(k\cdot n + n^2)\) per iteration, comparable to FastPAM but produces …