Abstract: Detection and classification of patterns in high speed streaming data using algorithmic learning processes creates transferable models. Synchronized local data models are housed in a data model repository and upon receiving one or more data points from a continual source of data a determination is made whether the newly collected data falls within an existing data model. If so, the detection is reported. If not the data is stored in an unknown data detection list. Clusters of the data residing in the unknown data list are formed and from those clusters statistical features extracted. An n-dimensional convex hull is fashioned bounding a region within which the statistical features lie thereby establishing a new class of data. The new class of data is, or can be, thereafter transferred to other existing models such that the receiving model can update its data model repository without performing any data analysis.