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Follow the links below to find material targeted to the unit's elements, performance criteria, required skills and knowledge

Elements and Performance Criteria

1. Determine data clustering requirements
   • Research organisation’s need for data clustering and define problem, objective and outputs
   • Determine required machine and input data set according to task requirements
   • Define evaluation protocol and accepted measure of success
   • Develop and document required benchmark model
   • Collect data according to task requirements
   • Evaluate data quantity, completeness and alignment according to task requirements
   • Transform and format data according to specifications
   • Finalise data preparation according to task requirements
   • Input raw data according to task requirements
   • Run required algorithm and adhere to required processing time frame
   • Obtain output reports and determine completeness of task according requirements
2. Finalise data clustering tasks
   • Analyse data report and determine clustering tasks have been completed according to task requirements
   • Interpret, summarise and document findings
   • Communicate findings to required personnel and seek and respond to feedback
   • Lodge documentation according to task requirements and finalise task activities according to organisational requirements

Performance Evidence

The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:

collect, prepare and cluster data using unsupervised machine learning methodologies and report on the findings on at least two occasions.

In the course of the above, the candidate must:

research industry standard approaches and methodologies for machine learning

evaluate and prepare data.

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Knowledge Evidence

The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:

methodologies for data clustering unlabelled data including intra-cluster cohesion and intra-cluster separation

industry standard data clustering methodologies including benchmark modelling techniques for data clustering

report writing methodologies relevant to reporting findings of data clustering activities

industry standard machine learning methodologies relevant to unsupervised learning

methodologies for modelling data relevant to unsupervised learning.

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