Clustering and outlier detection in anomaly and causation detection for computing environments is disclosed. An example method includes receiving an input stream having data instances, each of the data instances having multi-dimensional attribute sets, identifying any of outliers and singularities in the data instances, extracting the outliers and singularities, grouping two or more of the data instances into one or more groups based on correspondence between the multi-dimensional attribute sets and a clustering type, and displaying the grouped data instances that are not extracted in a plurality of clustering maps on an interactive graphical user interface, wherein each of the plurality of clustering maps is based on a unique clustering type.