Amazon Technologies, Inc.

Sathish Kumar Palanisamy

Srujana Merugu

Gowda Dayananda Anjaneyapura Range

Gourav Roy

Amit Chandak

Aswin Natarajan

Bharath Venkatesh

Prateek Gupta

Jagannathan Srinivasan

A number of attributes of different attribute types, to be used to assign observation records of a data set to clusters, are identified. Attribute-type-specific distance metrics for the attributes, which can be combined to obtain a normalized aggregated distance of an observation record from a cluster representative, are selected. One or more iterations of a selected clustering methodology are implemented on the data set using resources of a machine learning service until targeted termination criteria are met. A given iteration includes assigning the observations to clusters of a current version of a clustering model based on the aggregated distances from the cluster representatives of the current version, and updating the cluster representatives to generate a new version of the clustering model.

Scalable clustering for mixed machine learning data

An approximate data structure to represent clusters of observation records of a data set is identified. A hierarchical representation of a plurality of clusters, including the targeted number of clusters among which the observation records are to be distributed, is generated. Each node of the hierarchy comprises an instance of the approximate data structure. Until a set of termination criteria are met, iterations of a selected clustering methodology are run. In a given iteration, distances of observation records from the cluster representatives of a current version of the model are computed using the hierarchical representation, and a new version of the model with modified cluster representatives is generated.

Clustering sparse high dimensional data using sketches

Growing community of inventors

Dharmapuri, India

Sathish Kumar Palanisamy