Microaggregation

Microaggregation groups similar entries and replaces the original individual values with the group average. This method is most commonly used for numerical parameters, but there are also methods that allow micro-aggregation to be used for categorical data.

Example

Looking at the table obtained after the data deletion, it can be seen that this time micro-aggregation was used to transform the data of the respective columns. In this example, micro-aggregation has been applied to the parameter "age".
Original data
ID Age City Diagnosis
101 35 Sigulda Hypertension
102 28 Ape Diabetes
103 40 Dobele Migraine
104 32 Suntaži Multiple sclerosis
105 22 Riga Asthma
106 44 Liepaja Hypertension
Age grouped in dataset 20–29, 30-39 and 40-49, and an average value is calculated for each group, replacing the original entry.
Anonymised dataset by age micro-aggregation
ID Age City Diagnosis
101 34 Sigulda Hypertension
102 25 Ape Diabetes
103 42 Dobele Migraine
104 34 Suntaži Multiple sclerosis
105 25 Riga Asthma
106 42 Liepaja Hypertension
Of course, also before micro-aggregating the data, careful consideration must be given to whether the information processed in this way will allow the intended data analysis to be carried out.

Microaggregation

Microaggregation groups similar entries and replaces the original individual values with the group average. This method is most commonly used for numerical parameters, but there are also methods that allow micro-aggregation to be used for categorical data.

Example

Looking at the table obtained after the data deletion, it can be seen that this time micro-aggregation was used to transform the data of the respective columns. In this example, micro-aggregation has been applied to the parameter "age".
Original data
ID Age City Diagnosis
101 35 Sigulda Hypertension
102 28 Ape Diabetes
103 40 Dobele Migraine
104 32 Suntaži Multiple sclerosis
105 22 Riga Asthma
106 44 Liepaja Hypertension
Age grouped in dataset 20–29, 30-39 and 40-49, and an average value is calculated for each group, replacing the original entry.
Anonymised dataset by age micro-aggregation
ID Age City Diagnosis
101 34 Sigulda Hypertension
102 25 Ape Diabetes
103 42 Dobele Migraine
104 34 Suntaži Multiple sclerosis
105 25 Riga Asthma
106 42 Liepaja Hypertension
Of course, also before micro-aggregating the data, careful consideration must be given to whether the information processed in this way will allow the intended data analysis to be carried out.