Abstract: In one embodiment, data records associated with attributes are received. A check is made to determine whether the data records are greater than a maximum data record limit of a graph. Further, when the maximum number of data records in the bin is less than or equal to the maximum data record limit of the graph, the data records are retrieved and presented in the graph. When the data records are greater than the maximum data record limit of the graph, the data records are grouped into bins based on initial bin sizes corresponding to the plurality of attributes. Furthermore, weighted densities of the bins are determined using a maximum number of data records associated with a bin and a maximum data record limit of the bin. Further, the graph is rendered to present the weighted densities of the bins using a randomization technique to analyze the data records.

Abstract: Parallel co-ordinates associated with a business scenario represent underlying data of the business scenario. Based upon associated categorical variables, the parallel co-ordinates are divided into a plurality of segments. Each segment includes a pair of continuous variables represented as a pair of axes of the parallel co-ordinates. The pairs of axes of the parallel co-ordinates are fragmented to generate bi-variant bins. The bi-variant bines include continuous values corresponding to each fragment of the pair of axis. An associated backend database is queried to determine aggregated tuples of continuous values between each bi-variant bin of the corresponding pair of axes. Based on the aggregated tuples, a statistical visualization of the aggregated tuples between the bi-variant bins for the axes is generating. The statistical visualization includes a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

Abstract: Parallel co-ordinates associated with a business scenario represent underlying data of the business scenario. Based upon associated categorical variables, the parallel co-ordinates are divided into a plurality of segments. Each segment includes a pair of continuous variables represented as a pair of axes of the parallel co-ordinates. The pairs of axes of the parallel co-ordinates are fragmented to generate bi-variant bins. The bi-variant bines include continuous values corresponding to each fragment of the pair of axis. An associated backend database is queried to determine aggregated tuples of continuous values between each bi-variant bin of the corresponding pair of axes. Based on the aggregated tuples, a statistical visualization of the aggregated tuples between the bi-variant bins for the axes is generating. The statistical visualization includes a distribution of the tuples for the plurality of segments corresponding to the parallel co-ordinates.

Abstract: In one embodiment, data records associated with attributes are received. A check is made to determine whether the data records are greater than a maximum data record limit of a graph. Further, when the maximum number of data records in the bin is less than or equal to the maximum data record limit of the graph, the data records are retrieved and presented in the graph. When the data records are greater than the maximum data record limit of the graph, the data records are grouped into bins based on initial bin sizes corresponding to the plurality of attributes. Furthermore, weighted densities of the bins are determined using a maximum number of data records associated with a bin and a maximum data record limit of the bin. Further, the graph is rendered to present the weighted densities of the bins using a randomization technique to analyze the data records.

Abstract: In one embodiment, a plurality of data records is received. Further, the received plurality of data records are classified into one or more data clusters based on parameters associated with the plurality of data records. Furthermore, a visualization panel on a computer generated graphical user interface is presented for graphically indicating number of data records in a data cluster of the one or more data clusters, density of the data records in the data cluster and proximity between the one or more data clusters. Also, the visualization panel graphically displays parameters associated with the one or more data clusters and distribution of data in the data cluster of the one or more data cluster.