Abstract: Systems, methods, and other embodiments associated with identifying a document type of an unidentified document based on image features are described. In one embodiment, image pairs are formed by pairing the unidentified document with anchor images from a plurality of anchor images, wherein each anchor image is a known document type. For each image pair, first visual features are extracted from the unidentified document and second visual features are extracted from the paired anchor image. A similarity function is applied to compare the first visual features and the second visual features, and a similarity score is generated for each image pair based on the comparing. The most similar anchor image from the image pairs, which has a greatest similarity score, is identified. The document type of the unidentified document is then predicted as the known document type associated with the most similar anchor image.

Abstract: According to an aspect, a system performs a first machine learning (ML) technique and a second ML technique on a large set of data items. The first ML technique identifies a primary cluster containing data items classified as non-anomalous, with the rest of the data items classified as anomalous. The second ML technique identifies a set of secondary clusters. Upon receiving feedback on the classification of at least some of the data items, the system maintains a feedback data associating the feedback with each secondary cluster. Upon receiving an indication to find a classification of a data item, the system identifies as a result, whether the data item is non-anomalous or anomalous based on whether the data item belongs to the primary cluster or not. The system also determines based on a secondary cluster to which the data item belongs and the feedback associated with the secondary cluster, whether the result is to be modified.

Abstract: Method includes obtaining sample records from dataset associated with user and including records associated with identifiers customers of user; executing first clustering using sample records, to obtain first set of clusters for first identifiers associated with sample records, first clustering using features associated with first identifiers; providing visualization of first set of clusters; determining whether user input for optimizing first set of clusters provided in visualization is received; when user input for optimizing first set of clusters is not received, determining first information related to first set of clusters as final result information; when user input for optimizing first set of clusters is received: executing second clustering using sample records, to obtain second set of clusters for first identifiers, second clustering using features associated with first identifiers, and determining second information related to second set of clusters as final result information; and clustering entire

Abstract: Method includes obtaining sample records from dataset associated with user and including records associated with identifiers customers of user; executing first clustering using sample records, to obtain first set of clusters for first identifiers associated with sample records, first clustering using features associated with first identifiers; providing visualization of first set of clusters; determining whether user input for optimizing first set of clusters provided in visualization is received; when user input for optimizing first set of clusters is not received, determining first information related to first set of clusters as final result information; when user input for optimizing first set of clusters is received: executing second clustering using sample records, to obtain second set of clusters for first identifiers, second clustering using features associated with first identifiers, and determining second information related to second set of clusters as final result information; and clustering entire

Abstract: According to an aspect, a technology system maintains an account data specifying the details of multiple accounts including a specific set of accounts as being delinquent. Upon receiving a feature set to be used, the system clusters the multiple accounts into groups based on the feature set. The system generates a multi-classification model for assignment of agents to the accounts based on the group qualifiers associated with the groups and the feature set. The system then identifies a target group likely to present problems with debt collection, assigns, using the multi-classification model, a corresponding agent for each account of the group and determines a recommended recovery strategy for each assignment. The system provides the details of the group, the corresponding agents assigned and the recommended recovery strategy for each assignment.

Abstract: Systems, methods, and other embodiments associated with converting a static cluster data table to a graphical hierarchical tree are described. In one embodiment, a method includes recursively traversing the static cluster data table to identify a root cluster, identify child clusters from the root cluster and child clusters from each other that define parent-child relationships, and identify decision segments that caused a segment split of cluster data. A 2-dimensional visual hierarchy is generated and displayed in a graphical form using a plurality of nodes that represent the root cluster and the child clusters along with path lines that connect the nodes. The 2-dimensional visual hierarchy displays a hierarchical visualization of the static cluster data table that shows an order of decision segments that occurred to segment a dataset and how the dataset was segmented by a clustering algorithm leading to a final cluster of a leaf node.

Abstract: Systems, methods, and other embodiments associated with identifying a document type of an unidentified document based on image features are described. In one embodiment, image pairs are formed by pairing the unidentified document with anchor images from a plurality of anchor images, wherein each anchor image is a known document type. For each image pair, first visual features are extracted from the unidentified document and second visual features are extracted from the paired anchor image. A similarity function is applied to compare the first visual features and the second visual features, and a similarity score is generated for each image pair based on the comparing. The most similar anchor image from the image pairs, which has a greatest similarity score, is identified. The document type of the unidentified document is then predicted as the known document type associated with the most similar anchor image.

Abstract: According to an aspect, a technology system maintains an account data specifying the details of multiple accounts including a specific set of accounts as being delinquent. Upon receiving a feature set to be used, the system clusters the multiple accounts into groups based on the feature set. The system generates a multi-classification model for assignment of agents to the accounts based on the group qualifiers associated with the groups and the feature set. The system then identifies a target group likely to present problems with debt collection, assigns, using the multi-classification model, a corresponding agent for each account of the group and determines a recommended recovery strategy for each assignment. The system provides the details of the group, the corresponding agents assigned and the recommended recovery strategy for each assignment.