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Analysis of K-Means Clustering on Earthquake Data

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This assignment explores the application of K-Means clustering to analyze earthquake data. The project, conducted by students from the University of Houston, focuses on the interpretation of results using various parameters like epsilon (ε) and Minimum Points. Relevant resources include GIS maps and earthquake fault lines that enhance the understanding of seismic activity. The analysis aims to draw meaningful insights into earthquake patterns and distributions, providing a comprehensive overview of the dataset and the effectiveness of the K-Means algorithm in this context.

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Analysis of K-Means Clustering on Earthquake Data

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  1. COSC 6335 Data MiningFall 2009: Assignment3a Post Analysis Christoph F. Eick Department of Computer Science, University of Houston

  2. K-Means Complex9 Ruchika

  3. Faris K-Means

  4. Student Result Complex9

  5. Kaavya My values are epsilon = 0.0255 and m = 4.

  6. Earth Quake Backgroud http://www.geo.ua.edu/intro03/quakes.html http://www.cessind.org/images/images/earthquakeglobal.gif (skip!) http://www.data.scec.org/Module/storyline.html (important!) http://library.thinkquest.org/03oct/00758/en/disaster/earthquake/faultlines.html

  7. Student Results Earthquake Anarug:Earthquakedataset ε=0.028 MinPoints =10

  8. Le

  9. 3. Moustafa

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