Mining for Social Processes in Intelligence Data Streams

Mining for Social Processes in Intelligence Data Streams. Robert Savell, Ph.D. SBP ‘08 April 1,2008. 04/01/08. Overview:. Introduction: Process Based SNA. Process Detection and the Process Query System (PQS). Experiment: The Alibaba Data Set. Results. Conclusion.

523 views • 38 slides

Data Mining on Streams

Data Mining on Streams. Christos Faloutsos CMU. Dr. Deepay Chakrabarti (Yahoo) Dr. Spiros Papadimitriou (IBM) Prof. Byoung-Kee Yi (Pohang U.). Prof. Dimitris Gunopulos (UCR) Dr. Mengzhi Wang (Google). Thanks. For more info:. 3h tutorial, at:

1.43k views • 123 slides

Graphs, Data Mining, and High Performance Computing

Graphs, Data Mining, and High Performance Computing. Bruce Hendrickson Sandia National Laboratories, Albuquerque, NM University of New Mexico, Computer Science Dept. Outline. High performance computing Why current approaches can’t work for data mining

602 views • 40 slides

Mining Data Streams

Mining Data Streams . Data Streams. In many data mining situations, we know the entire data set in advance Stream Management is important when the input rate is controlled externally : Google queries Twitter or Facebook status updates

683 views • 41 slides

Mining High-Speed Data Streams

Mining High-Speed Data Streams. Hoeffding Trees and Very Fast Decision Trees. By: Mikael Weckstén. Introduktion. What is a decision tree. Given n training examples (x, y) where x is a vector i.e (x1, x2, x3... xi, y) Produce a model y = f(x). Introduktion cont. How is it structured.

467 views • 31 slides

Frequent Pattern Mining in Data Streams

Frequent Pattern Mining in Data Streams. Rishi Gosai. Frequent Pattern Analysis : Introduction. Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set

1.23k views • 47 slides

Mining High-Speed Data Streams

Mining High-Speed Data Streams. Pedro Domingos Geoff Hulten. Sixth ACM SIGKDD International Conference - 2000. Presented by: Tyler J. Sawyer UVM Spring 2014 - CS 332 Data Mining. 2. Outline. → Introduction → Hoeffding Trees → The VFDT System → Performance Study → Conclusion / Summary

602 views • 46 slides

Mining Data Streams

Mining Data Streams. The Stream Model Sliding Windows Counting 1’s. The Stream Model. Data enters at a rapid rate from one or more input ports. The system cannot store the entire stream. How do you make critical calculations about the stream using a limited amount of (secondary) memory?.

680 views • 27 slides

Active Mining of Data Streams

Active Mining of Data Streams. Wei Fan , Yi-an Huang , Haixun Wang and Philip S. Yu Proc. SIAM International Conference on Data Mining 2004. Speaker: Pei-Min Chou Date:2005/01/14. Introduction. In most real-world problems, labelled data streams are rarely immediately available

296 views • 18 slides

Mining Decision Trees from Data Streams

Mining Decision Trees from Data Streams. Thanks: Tong Suk Man Ivy HKU. Contents. Introduction: problems in mining data streams Classification of stream data VFDT algorithm Window approach CVFDT algorithm Experimental results Conclusions Future work. Data Streams. Characteristics

935 views • 56 slides

Data Mining: Concepts and Techniques Mining data streams

Data Mining: Concepts and Techniques Mining data streams. Chapter 8. Mining Stream, Time-Series, and Sequence Data. Mining data streams Mining time-series data Mining sequence patterns in transactional databases Mining sequence patterns in biological data. Mining Data Streams.

675 views • 17 slides

Mining Data Streams (Part 1)

573 views • 46 slides

Mining Data Streams

Mining Data Streams. The Stream Model Sliding Windows Counting 1’s. Data Management Versus Stream Management. In a DBMS, input is under the control of the programmer. SQL INSERT commands or bulk loaders. Stream Management is important when the input rate is controlled externally.

490 views • 27 slides

Mining High-Speed Data Streams

Mining High-Speed Data Streams. Presented by: Yumou Wang Dongyun Zhang Hao Zhou. Introduction. The world’s information is doubling every two years. From 2006 to 2011, the amount of information grew by a factor of 9 in just five years. Introduction.

870 views • 46 slides

Data Mining on Streams

Data Mining on Streams.

207 views • 12 slides

Mining Data Streams

Mining Data Streams. The Stream Model Sliding Windows Counting 1’s. Slides from Stanford CS345A, slightly modified. Data Management Versus Stream Management. In a DBMS, input is under the control of the programmer. SQL INSERT commands or bulk loaders.

364 views • 21 slides

Data Mining: Concepts and Techniques Mining data streams

Data Mining: Concepts and Techniques Mining data streams. Chapter 8. Mining Stream, Time-Series, and Sequence Data. Mining data streams Mining time-series data Mining sequence patterns in transactional databases Mining sequence patterns in biological data. Mining Data Streams.

216 views • 17 slides

Data Mining for Data Streams

Data Mining for Data Streams. Mining Data Streams. What is stream data? Why Stream Data Systems? Stream data management systems: Issues and solutions Stream data cube and multidimensional OLAP analysis Stream frequent pattern analysis Stream classification Stream cluster analysis

716 views • 65 slides

Mining Data Streams

Mining Data Streams. The Stream Model Sliding Windows Counting 1’s. The Stream Model. Data enters at a rapid rate from one or more input ports. The system cannot store the entire stream. How do you make critical calculations about the stream using a limited amount of (secondary) memory?.

308 views • 27 slides

Mining Data Streams

Mining Data Streams. What is stream data? Why Stream Data Systems? Stream data management systems: Issues and solutions Stream data cube and multidimensional OLAP analysis Stream frequent pattern analysis Stream classification Stream cluster analysis.

2.03k views • 178 slides

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

Advanced Data Mining Tools: Fractals and Power Laws for Graphs, Streams and Traditional Data. Christos Faloutsos Carnegie Mellon University. THANK YOU!. Prof. Jiawei Han Prof. Kevin Chang. Overview. Goals/ motivation: find patterns in large datasets: (A) Sensor data

848 views • 83 slides