This disclosure relates generally to adaptive learning based systems and methods for optimization of unsupervised clustering. The embodiments of present disclosure herein address unresolved problem of involving manual intervention in data preparation, annotating or labelling training data to train classifiers, and taking a number of clusters directly as an input from the users for data classification. The method of the present disclosure provides a fully unsupervised optimized approach for auto clustering of input data that automatically determines the number of clusters for the input data by leveraging concepts of graph theory and maximizing a cost function. The method of present disclosure is capable of handling a new data by continuously and incrementally improving the clusters. The method of present disclosure is domain agnostic, scalable, provides expected level of accuracy for real-world data, and helps in minimizing utilization of powerful processors leading to reduced overall cost.

An electronic device includes a housing, a communication module positioned inside the housing, a processor positioned inside the housing and operatively connected with the communication module, a sensor module operatively connected with the processor, and a memory positioned inside the housing and operatively connected with the communication module, the sensor module, and the processor. The memory stores instructions configured to, when executed, enable the processor to gather data related to a first user, send a request for a user group corresponding to a first category among a plurality of categories to an external server using the communication module, obtain the user group corresponding to the first category based on at least part of the data related to the first user from the external server using the communication module, and provide information about at least one second user in the obtained user group.

An electronic device includes a housing, a communication module positioned inside the housing, a processor positioned inside the housing and operatively connected with the communication module, a sensor module operatively connected with the processor, and a memory positioned inside the housing and operatively connected with the communication module, the sensor module, and the processor. The memory stores instructions configured to, when executed, enable the processor to gather data related to a first user, send a request for a user group corresponding to a first category among a plurality of categories to an external server using the communication module, obtain the user group corresponding to the first category based on at least part of the data related to the first user from the external server using the communication module, and provide information about at least one second user in the obtained user group.

Systems and methods of improved network analytics are disclosed. A system may determine feature propagation in a network of nodes of a graph database. The system may compute, at scale, datasets having complex relationships using graph analysis to determine network effects of entities in a network of entities stored in a graph database. The system may identify entities of interest, which may be associated with a quantitative feature value. The system may compute paths from an entity to the entities of interest, centrality metrics for entities in each of the paths, and path lengths to determine network effects of the entity of interests on the entity. The system may use the computed network effects, taking into account types of relationships between entities in the paths, to determine scaled quantitative feature values for the entity that is subject to the network effects of the entities of interest.

A request is received to temporarily identify nodes that are related to one or more selected nodes of a plurality of nodes for a data graph data structure that includes a plurality of relationships between a plurality of nodes arranged in a predetermined spatial arrangement. One or more related nodes that are related to the one or more selected nodes are identified. A display generates only the one or more selected nodes and each of the one or more related nodes and the relationships thereof in an updated spatial arrangement that defines relatively less cumulative space between the one or more selected nodes and the one or more related nodes than the predetermined spatial arrangement. A request to stop generating the updated spatial arrangement is received. The display stops generating the predetermined spatial arrangement in response to receiving the request to stop generating the updated spatial arrangement.

As described herein, a system, method, and computer program are provided for synthesizing user transactional data for de-identifying sensitive information. In use, transactional data of a plurality of users is identified. Additionally, the plurality of users are clustered based on the transactional data, to form groups of users having transactional data representing similar transactional behavior. Further, synthesized transactional data is generated for the users in each group by: identifying a subset of the transactional data that corresponds to the users in each group, shuffling the transactional data in the subset across the users in each group, and perturbing portions of the shuffled transactional data.

As described herein, a system, method, and computer program are provided for synthesizing user transactional data for de-identifying sensitive information. In use, transactional data of a plurality of users is identified. Additionally, the plurality of users are clustered based on the transactional data, to form groups of users having transactional data representing similar transactional behavior. Further, synthesized transactional data is generated for the users in each group by: identifying a subset of the transactional data that corresponds to the users in each group, shuffling the transactional data in the subset across the users in each group, and perturbing portions of the shuffled transactional data.

A method for training a neural network to cluster multiple people in an image into groups and estimate an activity of each of the groups is provided. The method extracts a feature of each of the multiple people in the image. The method inputs the feature to the neural network to estimate an affinity matrix A and the activity of each of the groups. The method calculates a first loss between the estimated affinity matrix and a ground truth affinity matrix for the image, and a second loss between the estimated activity of each of the groups and a ground truth activity of each of the groups for the image. The first loss is calculated using Maximum Spanning Trees. The method trains the neural network based on the first and second losses.

A method for training a neural network to cluster multiple people in an image into groups and estimate an activity of each of the groups is provided. The method extracts a feature of each of the multiple people in the image. The method inputs the feature to the neural network to estimate an affinity matrix A and the activity of each of the groups. The method calculates a first loss between the estimated affinity matrix and a ground truth affinity matrix for the image, and a second loss between the estimated activity of each of the groups and a ground truth activity of each of the groups for the image. The first loss is calculated using Maximum Spanning Trees. The method trains the neural network based on the first and second losses.

The present invention provides a data network, a data ingestion system and a method of data ingestion in the data network for a supply chain management enterprise application. The data network includes one or more data objects of different data types received from different data sources structured on multiple distinct architecture, connected to each other for executing multiple functions in the enterprise application.