Tata Consultancy Services Limited

Sandip Jadhav

Harrick Mayank Vin

Mohan Raj Velayudhan Kumar

Rahul Ramesh Kelkar

Akshay Kumar Singhal

Pradeep Unde

This disclosure relates generally to performing user segmentation, and more particularly to predicting attribute values for user segmentation. In one embodiment, the method includes segregating a user with an incomplete attribute value and a user with complete attribute values for an attribute into a first group and a second group respectively, computing prior probability for each suggestive attribute value, identified for the incomplete attribute value, based on number of users in second group having the suggestive attribute value as attribute value for the attribute. Computing likelihood for each suggestive attribute value based on similarity of the attribute values of the user of the first group with users of the second group, computing a posterior probability for each suggestive attribute value based on the prior probability and the likelihood, selecting a suggestive attribute value with the highest posterior probability as the attribute value for the incomplete attribute value of the user.

Predicting attribute values for user segmentation by determining suggestive attribute values

Described herein, is a method for user segmentation for cost-based deployment of enterprise infrastructure. According to an implementation, for each of the users, a user fingerprint (UFP) score set comprising rating scores that quantify levels of enterprise-level needs of the each user is determined. For each of deployment solutions of the enterprise, a deployment fingerprint (DFP) score set comprising rating scores that quantify levels of capabilities of the each deployment solution enabled to meet enterprise-level needs of the users is determined. The users are grouped into user segments based on the UFP score sets of the users. A definition of each of the user segments is determined. The definition is indicative of a user whose enterprise-level needs define characteristics of the corresponding user segment. A number of user segments is found based on a cost associated with the deployment solutions for the user segments.

User segmentation for cost-based deployment of enterprise infrastructure

The present invention relates to a configurable parameter driven system and method for providing an appropriate target operating environment based on user specific needs and enterprise objectives. The configuration parameters can be changed to account for newer computing environment solutions that could appear and could also be tailored for enterprise specific needs. The method fingerprint the end users' based on characteristics and requirements to derive user needs and enterprise criteria's. The method is systematic and flexible amenable to change in varying enterprise environment.

System and method of deriving appropriate target operating environment

Methods and systems for Assurance-enabled Linde Buzo Gray (ALBG) data clustering is described herein. In an implementation, a user model data from a database available to the processor is obtained. The user model data comprises data elements or users, each of which corresponds to features and feature values associated with the users. These data elements of the user model data are segmented into clusters using our segmentation approach with an initial accuracy criterion parametric value and the output is captured as segment data. The segment data output is checked for initial pareto validity. If successful, iterative segmentation run with incremental accuracy criterion using parameterized value is performed till the segmented clusters are determined valid against pareto validity check. The last successful pareto valid segmented cluster data is considered as the finalized segment output data. For an invalid initial pareto validity check, a segmentation run with a pre-determined accuracy criterion value is done to arrive at the finalized segment output data.

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Pune, India

Sandip Jadhav