How can I reduce the number of dimensions using a Clustering algorithm in a mixed dataset?
Some common techniques to reduce number of features:

Missing Values Ratio. Data columns with too many missing values are unlikely to carry much useful information. Thus data columns with number of missing values greater than a given threshold can be removed. The higher the threshold, the more aggressive the reduction.

Low Variance Filter. Similarly to the previous technique, data columns with little changes in the data carry little information. Thus all data columns with variance lower than a given threshold are removed. A word of caution: variance is range dependent; therefore normalization is required before applying this technique.

Random Forests / Ensemble Trees. Decision Tree Ensembles, also referred to as random forests, are useful for feature selection in addition to being effective classifiers. Personally I will prefer this method as it is easy to implement. Example use is given in the kernel --&gt;
https://www.kaggle.com/prashant111/xgboost-k-fold-cv-feature-importance?scriptVersionId=48823316&amp;cellId=74

High Correlation Filter. Data columns with very similar trends are also likely to carry very similar information. In this case, only one of them will suffice to feed the machine learning model. Here we calculate the correlation coefficient between numerical columns and between nominal columns as the Pearson’s Product Moment Coefficient and the Pearson’s chi square value respectively. Pairs of columns with correlation coefficient higher than a threshold are reduced to only one. A word of caution: correlation is scale sensitive; therefore column normalization is required for a meaningful correlation comparison.


Refer : https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html