Workshop
Workshop
Cluster Analysis 1-Day Course
Cluster analysis is a type of data analysis used to categorize and label sets of data. It is widely used in Machine Learning and general statistical analysis. Cluster analysis is simply assessing the data and looking for groups (clusters) in the data set based on criteria se by the analyst. Cluster analysis has been used for decades under other names. In soils sampling it is common and is usually included in a geochemical analysis methods set in most specialist software.
Cluster analysis is used to classify data when manual labelling is not feasible or where boundaries. It is necessary when there is a large amount of data or data has multiple variables that need to be considered. Cluster analysis methods are also good for comparing new data to established categories and placing them in the correct groups.
Cluster analysis requires the use of computers for the grouping and labelling of the data as the data sets are generally too large or complex for manual analysis and labelling in a reasonable amount if time. Previous good coding skills or specialist software was required by analysts. With the wider availably of statistical packages in “R ”and Python open source software the barriers to learning these methods are now much lower.
This course looks at the two most popular methods for Cluster Analysis, Hierarchal Clustering and K-Means Clustering. The course will cover the basic theory on the calculations and present several use cases including outlier detection, material types classification and stratigraphic identification in boreholes.
The course is Python based coding and requires very basic coding skills. The exercises are designed to be run by delegates with beginner level skills.
The course content is outlined below.
Day 1
Introduction to Python
Software Setup and Introduction to Python and Jupyter Lab/Notebook – Instructions for loading the software before the course will be provided. A free Python intro course will also be provided for people who have never used Python. This takes about one afternoon to complete.
The course doe not require programming skills to complete. The scripts are designed to be run in full without additional code being written.
Hierarchical Clustering
- Methodology-basic explanation of the method with advantages and limitations
- Examples from two data sets using the seaborn clustermap method
- Extracting results and exporting to geological modelling software
k-means Clustering
- Methodology--basic explanation of the method with advantages and limitations
- Examples from two data sets using the seaborn clustermap method
- Data and Results Visualization
- Exporting labelled Data
- Extracting results and exporting to geological modelling software
Data transformations
- Discussion on transforming data before the cluster analysis. When it is needed and when it should not be done.
- Problems with transition metals especially Fe and Mn
- Rescaling--z-score
- Gaussian transforms
- Centered log Ratios
- Other “normalization” options
Data used in this course will be multivariate data from base metals or bulk materials deposits and lithogeochemical data from public databases. Cluster analysis is intended for stratigraphic and lithological definition and, mapping of rock types and domaining. One of the use cases with be quality control and outlier identification.
Experience Requirements
This course is aimed at exploration Geologists, Mineral Resource Specialists a The course is set up to teach practical field methods and is not academically rigorous. It is assumed that delegates have practical research or field experience. It is also assumed that delegates have a basic understanding of statistics and have some exposure to data analytics in geology, or geochemistry. Experience with a coding language is helpful but not essential. All computer code will be provided and the exercises guided.
Computer and Software requirements
- Materials
- Laptop computer with administrative rights for installing the Anaconda and Python software -16GB RAM and a decent graphics card (gaming cards are ok)
- Delegates will need to be able to access the venue WiFi internet for some parts of the course.
- Software
- Microsoft Excel (there are graphics and formulas that may not work as intended in Google Sheets)
- Anaconda (Python) for Jupyter Notebook (recommended for beginners or inexperienced coders) or VS_code(experience with coding in VS code required)
- PDF reader
Instructions for the installation of software will be provided before the Course. Delegates with corporate computers may need to get help from their IT departments in cases where the installation of external software is blocked by their IT system.
Workshop Sponsor
Presenters
- Kathleen Body and Dennis from Dekka Dynamics.
Registration
Workshops must be purchased with a conference registration. If you wish to include the Automating Resource Modelling Workflows workshop to your registration, please select the additional workshop option in your registration form.
If you have already registered to attend the Mineral Resource Estimation Conference and wish to add the workshop to your registration, please contact conference@ausimm.com.
PD Hours
X
Date
Thursday, 8th May 2025
Time
8.30am registrations
9.00am workshop starts
5.00pm workshop finishes
Location
Dekka Dynamics, Perth
Cost
AusIMM Member – $700 (incl. GST)
Non Member – $900 (incl. GST)
Numbers
Max 18
Catering
Morning, afternoon tea and lunch included
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