Abstract: A computer-implemented method, which comprises the following: aggregating a plurality of records in accordance with an aggregation specification, wherein the records are part of a dimensionally-modeled fact collection; graphically representing the records in un-aggregated form; graphically representing the records in aggregated form; and causing the graphical representation of the records to be switched between aggregated form and un-aggregated form based on a user indication.

Abstract: An unsupervised classification approach is improved by imposing some order into the treatment of the records and their attributes, which otherwise would be treated as random variables. A method is provided to identify particular attributes that are most associated with the “good” records within each of the plurality of groups of records within a data set. Based on a supervised scoring method, the records of the data set are processed to indicate their measure of “goodness”. There are various ways by which the records can be processed to indicate a bias during unsupervised clustering processing.

Abstract: Records representing items in a dimensionally-modeled fact collection are assigned to bins. A count-based portion of a user interface receives user bin assignment specification of the records based on user-specified counts of records. Actual counts for bin assignment are determined by constraining records having a same data value at a specified particular dimension to be within the same bin. A user-observable indication of the determined actual counts is provided. The user interface may include a value-based portion. The value-based portion of the user interface may be operated to receive user indication of bin assignment specification of records based on user-specified at least one value at the particular dimension. Determining actual counts includes reconciling the user indication of bin assignment specification in the count-based portion with the user indication of bin assignment specification in the value-based portion.

Abstract: A computer-implemented method, which comprises the following: receiving a graphical selection of a subset of data points from a set of data points, each data point representing at least one record of a dimensionally-modeled fact collection; and exporting information associated with the selected subset of data points.

Abstract: A method for visually representing a plurality of clusters, the plurality of clusters comprising a plurality of entities with respect to a plurality of entity attributes is provided. The plurality of entities is segmented into the plurality of clusters, such that each individual entity belongs to at least one cluster. A plurality of entity data regarding the plurality of entities is processed to obtain a plurality of characteristics of each cluster of the plurality of clusters with respect to each entity attribute of the plurality of entity attributes. A visual display of the plurality of clusters with respect to the plurality of entity attributes is generated, such that for each cluster of the plurality of clusters and for each entity attribute of the plurality of entity attributes, a portion of the display represents at least two characteristics of the plurality of characteristics simultaneously for that cluster with respect to that entity attribute.

Abstract: Records representing items in a dimensionally-modeled fact collection are assigned to bins. A count-based portion of a user interface receives user bin assignment specification of the records based on user-specified counts of records. Actual counts for bin assignment are determined by constraining records having a same data value at a specified particular dimension to be within the same bin. A user-observable indication of the determined actual counts is provided. The user interface may include a value-based portion. The value-based portion of the user interface may be operated to receive user indication of bin assignment specification of records based on user-specified at least one value at the particular dimension. Determining actual counts includes reconciling the user indication of bin assignment specification in the count-based portion with the user indication of bin assignment specification in the value-based portion.

Abstract: A computer-implemented method, which comprises the following: receiving a graphical selection of a subset of data points from a set of data points, each data point representing at least one record of a dimensionally-modeled fact collection; and exporting information associated with the selected subset of data points.

Abstract: A computer-implemented method, which comprises the following: aggregating a plurality of records in accordance with an aggregation specification, wherein the records are part of a dimensionally-modeled fact collection; graphically representing the records in un-aggregated form; graphically representing the records in aggregated form; and causing the graphical representation of the records to be switched between aggregated form and un-aggregated form based on a user indication.

Abstract: A method for visually representing a plurality of clusters, the plurality of clusters comprising a plurality of entities with respect to a plurality of entity attributes is provided. The plurality of entities is segmented into the plurality of clusters, such that each individual entity belongs to at least one cluster. A plurality of entity data regarding the plurality of entities is processed to obtain a plurality of characteristics of each cluster of the plurality of clusters with respect to each entity attribute of the plurality of entity attributes. A visual display of the plurality of clusters with respect to the plurality of entity attributes is generated, such that for each cluster of the plurality of clusters and for each entity attribute of the plurality of entity attributes, a portion of the display represents at least two characteristics of the plurality of characteristics simultaneously for that cluster with respect to that entity attribute.

Abstract: A method for selecting at least one target customer attribute from a plurality of customer attributes, wherein each customer attribute represents a unique customer characteristic is provided. The plurality of customer attributes is presented for selection. A selection of at least one target customer attribute selected from the plurality of customer attributes is received. For each cluster of a plurality of clusters, indicated the statistic of customers belonging to that cluster who possess each and every one of the at least one target customer attribute. The plurality of clusters comprises a plurality of customers and each customer of the plurality of customers belongs to at least one cluster of the plurality of clusters.

Abstract: An unsupervised classification approach is improved by imposing some order into the treatment of the records and their attributes, which otherwise would be treated as random variables. A method is provided to identify particular attributes that are most associated with the “good” records within each of the plurality of groups of records within a data set. Based on a supervised scoring method, the records of the data set are processed to indicate their measure of “goodness”. There are various ways by which the records can be processed to indicate a bias during unsupervised clustering processing.