how to handle outliers for clustering algorithms?

you can perform standardization of your data using Standard Scaler before applying clustering techniques or you can use k-mediod clustering algorithm. You can also use z-score analysis to remove your outliers.

If you have outliers, the best way is to use a clustering algorithm that can handle them.

For example DBSCAN clustering is robust against outliers when you choose minpts large enough. Don't use k-means: the squared error approach is sensitive to outliers. But there are variants such as k-means-- for handling outliers.