Abstract: A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The methods and systems reduce the n-dimensional feature space to a lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension, which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

Abstract: Embodiments herein provide a method and system that determines an underlying social network from user interaction graphs based on mobile service usage and derive social influence scores for various contexts based on user interaction parameters. The present disclosure pertains to a method of determining social influence score for a user of a social network, said method comprising creating, using a social network analyzer, a social graph based on usage data generated by a plurality of users, said usage data being obtained from a mobile communication network, wherein vertices of the created social graph represent the plurality of users along with edge weights that are based on weighted linear or non-linear combinations of key performance indicators (KPIs) representing actions made by each user; and deriving, by the social network analyzer, influencer score for each user in the social graph based on user interactions from online social networks and mobile interaction patterns.

Abstract: Systems and methods for management of multi-perspective customer segments. This invention relates to customer management, and more particularly to management of customer segments, wherein the customer segments can be multi-perspective. Embodiments herein disclose methods and systems for generating multiple sets of clusterings of customers based on at least one customer attribute, and combining and interactively visualizing the clusterings, to derive insights.

Abstract: Disclosed herein are methods and systems for providing multivariate time series clustering for customer segmentation. The system comprises of a model management unit that devices a customer segmentation procedure based on temporal variations of user preferences, using MTS clustering, and utilize the discovered clusters to learn association rules specific to each clusters, and improves campaign targeting. The order of the VAR model is fixed based on the nature of the data and length of the time series.

Abstract: A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The methods and systems reduce the n-dimensional feature space to a lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension, which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

Abstract: Systems and methods for management of multi-perspective customer segments. This invention relates to customer management, and more particularly to management of customer segments, wherein the customer segments can be multi-perspective. Embodiments herein disclose methods and systems for generating multiple sets of clusterings of customers based on at least one customer attribute, and combining and interactively visualizing the clusterings, to derive insights.

Abstract: Disclosed herein are methods and systems for providing multivariate time series clustering for customer segmentation. The system comprises of a model management unit that devices a customer segmentation procedure based on temporal variations of user preferences, using MTS clustering, and utilize the discovered clusters to learn association rules specific to each clusters, and improves campaign targeting. The order of the VAR model is fixed based on the nature of the data and length of the time series.

Abstract: Embodiments herein provide a method and system that determines an underlying social network from user interaction graphs from mobile service usage and then deriving social influence scores for various contexts based on user interaction parameters.

Abstract: System and method for automatic discovery, annotation and visualization of customer segments and migration characteristics. Embodiments herein relate to customer management, and more particularly to segmenting customers based on value and analyzing segment migration of customers. Embodiments herein disclose segmentation of customers performed using value and behavioral attributes with the analyst/marketer providing the bin definitions for each feature or the bin ranges being automatically discovered. Embodiments herein also disclose automatic discovery of the number of segments using frequent pattern mining. Embodiments herein also disclose automatic annotation and visualization of segments that helps in interpreting the segments better. Embodiments herein enable designing of marketing campaigns considering the customer value as well as his behavioral attributes. Embodiments herein analyze segment migration and measure the value impact of migration trends.

Abstract: A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The method and system reduces the n-dimensional feature space to lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

Abstract: A mobile social networking platform including a system and method for cellular communications and accessing services using cellular communications. The system and method enable any mobile device user having a mobile station (MS) with minimal functionality to SMS to access a social network without installing any additional software on the MS. The system and method also enable the mobile user to access Location Based Services even if the network provides location information, without specifically requiring a GPS/GPRS enabled MS.

Abstract: Differentiating wireless uplink bandwidth request by connection priority. Embodiments herein relate to wireless communications, and more particularly to bandwidth management in wireless communications. Embodiments herein focus on multiplexing bandwidth requests on a common random access channel; specifically on contention resolution protocols for a common random access channel. Embodiments herein propose a differentiated back-off timer scheme to address the problems of QoS insensitivity and delay inequity, a set of back-off parameters to implement this scheme and a method of implementing existing well-known back-off strategies within this scheme's framework.

Abstract: The embodiments herein relate to user data management in a telecommunications network and, more particularly, to classifying users in a telecommunications network and subsequently leveraging the classification and augmented statistical information. The system uses intelligent modeling techniques & machine learning algorithms to classify users. It also groups users by statistical analysis of this classification. The system is able to provide secure, authenticated and authorized access to this classification, statistical grouping and other augmented information about users to an external agent in real-time. This enables service personalization and personalized service recommendations. System allows external agents to define certain classification criteria for users in the form of models, which are pluggable in nature, to derive multiple user classification schemes.

Abstract: A mobile social networking platform including a system and method for cellular communications and accessing services using cellular communications. The system and method enable any mobile device user having a mobile station (MS) with minimal functionality to SMS to access a social network without installing any additional software on the MS. The system and method also enable the mobile user to access Location Based Services even if the network provides location information, without specifically requiring a GPS/GPRS enabled MS.

Abstract: Differentiating wireless uplink bandwidth request by connection priority. Embodiments herein relate to wireless communications, and more particularly to bandwidth management in wireless communications. Embodiments herein focus on multiplexing bandwidth requests on a common random access channel; specifically on contention resolution protocols for a common random access channel. Embodiments herein propose a differentiated back-off timer scheme to address the problems of QoS insensitivity and delay inequity, a set of back-off parameters to implement this scheme and a method of implementing existing well-known back-off strategies within this scheme's framework.