9 Kdd2001 PPTs View free & download

Critique of the dirty dozen: 12 years of KDD PowerPoint PPT Presentation

Critique of the dirty dozen: 12 years of KDD - Critique of the dirty dozen: 12 years of KDD Daryl Pregibon AT&T Shannon Laboratory daryl@research.att.com KDD2001 San Francisco, CA Summary There remains tremendous ...

Critique of the dirty dozen: 12 years of KDD Daryl Pregibon AT&T Shannon Laboratory daryl@research.att.com KDD2001 San Francisco, CA Summary There remains tremendous ...

| PowerPoint PPT presentation | free to download

Advanced Data Mining Tools: Fractals and Power Laws for Graphs, Streams and Traditional Data PowerPoint PPT Presentation

Advanced Data Mining Tools: Fractals and Power Laws for Graphs, Streams and Traditional Data - road conditions / traffic monitoring. Automobile traffic. 0. 200. 400. 600. 800. 1000. 1200 ... 40. School of Computer Science. Carnegie Mellon. The Peer-to ...

road conditions / traffic monitoring. Automobile traffic. 0. 200. 400. 600. 800. 1000. 1200 ... 40. School of Computer Science. Carnegie Mellon. The Peer-to ...

| PowerPoint PPT presentation | free to download

Data%20Mining%20using%20Fractals%20and%20Power%20laws PowerPoint PPT Presentation

Data%20Mining%20using%20Fractals%20and%20Power%20laws - cars. UNC, 2005. C. Faloutsos. 7. School of Computer Science. Carnegie Mellon ... (and std - infinity (!)) degree. count. avg: 3.3. UNC, 2005. C. Faloutsos. 39 ...

cars. UNC, 2005. C. Faloutsos. 7. School of Computer Science. Carnegie Mellon ... (and std - infinity (!)) degree. count. avg: 3.3. UNC, 2005. C. Faloutsos. 39 ...

| PowerPoint PPT presentation | free to download

Data Mining using Fractals and Power laws PowerPoint PPT Presentation

Data Mining using Fractals and Power laws - Boston U., 2005. C. Faloutsos. 1. School of Computer Science. Carnegie Mellon ... Boston U., 2005. C. Faloutsos. 2. School of Computer Science. Carnegie Mellon ...

Boston U., 2005. C. Faloutsos. 1. School of Computer Science. Carnegie Mellon ... Boston U., 2005. C. Faloutsos. 2. School of Computer Science. Carnegie Mellon ...

| PowerPoint PPT presentation | free to download

Mining Biological Data PowerPoint PPT Presentation

Mining Biological Data - Each object or attribute in a coherent cluster may bear some relative bias (that ... Strategy: find the maximum coherent attribute sets for each pair of objects with ...

Each object or attribute in a coherent cluster may bear some relative bias (that ... Strategy: find the maximum coherent attribute sets for each pair of objects with ...

| PowerPoint PPT presentation | free to view

Data Mining using Fractals and Power laws - self-managing storage. infrastructure. a storage brick (0.5 5 TB) ~1 PB ' ... Self-* Storage (Ganger ) C. Faloutsos. 11. School of Computer Science. Carnegie Mellon ...

self-managing storage. infrastructure. a storage brick (0.5 5 TB) ~1 PB ' ... Self-* Storage (Ganger ) C. Faloutsos. 11. School of Computer Science. Carnegie Mellon ...

| PowerPoint PPT presentation | free to download

Indexing and Data Mining in Multimedia Databases PowerPoint PPT Presentation

Indexing and Data Mining in Multimedia Databases - Data mining & fractals. Road map. Motivation problems / case study. Definition of fractals and power laws. Solutions to posed problems. More examples. USC 2001 ...

Data mining & fractals. Road map. Motivation problems / case study. Definition of fractals and power laws. Solutions to posed problems. More examples. USC 2001 ...

| PowerPoint PPT presentation | free to view

$Data Mining using Fractals (fractals for fun and profit) PowerPoint PPT Presentation$

Data Mining using Fractals (fractals for fun and profit) - Data Mining using Fractals (fractals for fun and profit) ... New tools: power laws, self-similarity and fractals' work, where traditional assumptions fail ...

Data Mining using Fractals (fractals for fun and profit) ... New tools: power laws, self-similarity and fractals' work, where traditional assumptions fail ...

| PowerPoint PPT presentation | free to download

KDD2001 - ex: user1 A1,A2,B1,B2,C1,C2,B3,B4,C3,A3. user2 C1,C2,C3,C4(buy in C4) Contradiction: user1:C1,C2,C3- not buy. user2:C1,C2,C3- buy ...

ex: user1 A1,A2,B1,B2,C1,C2,B3,B4,C3,A3. user2 C1,C2,C3,C4(buy in C4) Contradiction: user1:C1,C2,C3- not buy. user2:C1,C2,C3- buy ...

| PowerPoint PPT presentation | free to view

Kdd2001 PowerPoint PPT Presentations