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# Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset

Examples based on a random data set (see example code below), illustrating some of the differences between the K-means and C-means clustering methods as implemented in R. Next time an example with soil profile data collected from the Pinnacles National Monument soil survey efforts. An online version of the PINN soil survey will be available soon here.

**Articles:**

- Quick tutorial on clustering approaches
- Lecture on K-means algorithm
- Notes on hierarchical clustering

Figure 1. Two class example |
Figure 2: Four class example |
Figure 3: 2-way fuzzy membership |

**Example in R:**

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## Software

- General Purpose Programming with Scripting Languages
- LaTeX Tips and Tricks
- PostGIS: Spatially enabled Relational Database Sytem
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- GDAL and OGR: geodata conversion and re-projection tools
- R: advanced statistical package
- Access Data Stored in a Postgresql Database
- Additive Time Series Decomposition in R: Soil Moisture and Temperature Data
- Aggregating SSURGO Data in R
- Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset
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- Comparison of Slope and Intercept Terms for Multi-Level Model
- Comparison of Slope and Intercept Terms for Multi-Level Model II: Using Contrasts
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- Exploration of Multivariate Data
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- Plotting XRD (X-Ray Diffraction) Data
- Using lm() and predict() to apply a standard curve to Analytical Data
- Working with Spatial Data
- Comparison of PSA Results: Pipette vs. Laser Granulometer

- GRASS GIS: raster, vector, and imagery analysis
- Generic Mapping Tools: high quality map production