Abstract: A computing device to compute clusters using random subsets of variables is provided. Each data point of a plurality of data points is associated with a variable to define a plurality of variables. A subset of the plurality of variables is randomly selected. The subset does not include all of the plurality of variables. A number of clusters into which to segment the received data is determined. Cluster data that defines each cluster of the determined number of clusters is determined by executing a clustering algorithm with the received data using only the plurality of data points defined for each observation that are associated with the randomly selected subset of the plurality of variables. The determined cluster data is stored to cluster second data into the determined number of clusters. The second data is different from the received data.

Abstract: A computing device to compute composite clusters is provided. A first and a second plurality of centroid locations are computed by executing a clustering algorithm with a first portion of data and a first input parameter and a second portion of the data and a second input parameter, respectively. The first portion is different from the second portion or the first input parameter is different from the second input parameter. A plurality of composite centroid locations is computed using the computed first and second plurality of centroid locations to define a composite set of clusters. An observation is selected. A cluster of the composite set of clusters to which to assign the observation is determined using the plurality of composite centroid locations. The selecting and the determining is repeated with each observation of the plurality of observations as the observation to define cluster assignments for the plurality of observations.

Abstract: A computing device to assign observations to clusters based on a statistical probability is provided. A first cluster assignment is defined by assigning the plurality of observations to a first set of clusters. A second cluster assignment is defined by assigning the plurality of observations to a second set of clusters. A set of composite clusters is defined based on the defined first set of clusters and the defined second set of clusters. For each observation, a statistical probability value for assigning an observation to each composite cluster of the defined set of composite clusters is computed based on the first and second cluster assignments and a composite cluster assignment is defined by assigning the observation to a cluster of the set of composite clusters based on the computed statistical probability value. The defined composite cluster assignment is stored.

Abstract: A computing device presents a cluster visualization based on a neural network computation. First centroid locations are computed for first clusters. Second centroid locations are computed for second clusters. Each centroid location includes a plurality of coordinate values where each coordinate value relates to a single variable of a plurality of variables. Distances are computed pairwise between each centroid location. An optimum pairing is selected based on a minimum distance of the computed pairwise distances where each pair is associated with a different cluster of a set of composite clusters. Noised centroid location data is created. A multi-layer neural network is trained with the noised centroid location data. A projected centroid location is determined in a multidimensional space for each centroid location as values of hidden units of a middle layer of the multi-layer neural network. A graph is presented for display that indicates the determined, projected centroid locations.

Abstract: A computing device to assign observations to clusters based on a statistical probability is provided. A first cluster assignment is defined by assigning the plurality of observations to a first set of clusters. A second cluster assignment is defined by assigning the plurality of observations to a second set of clusters. A set of composite clusters is defined based on the defined first set of clusters and the defined second set of clusters. For each observation, a statistical probability value for assigning an observation to each composite cluster of the defined set of composite clusters is computed based on the first and second cluster assignments and a composite cluster assignment is defined by assigning the observation to a cluster of the set of composite clusters based on the computed statistical probability value. The defined composite cluster assignment is stored.

Abstract: A computing device presents a cluster visualization based on a neural network computation. First centroid locations are computed for first clusters. Second centroid locations are computed for second clusters. Each centroid location includes a plurality of coordinate values where each coordinate value relates to a single variable of a plurality of variables. Distances are computed pairwise between each centroid location. An optimum pairing is selected based on a minimum distance of the computed pairwise distances where each pair is associated with a different cluster of a set of composite clusters. Noised centroid location data is created. A multi-layer neural network is trained with the noised centroid location data. A projected centroid location is determined in a multidimensional space for each centroid location as values of hidden units of a middle layer of the multi-layer neural network. A graph is presented for display that indicates the determined, projected centroid locations.

Abstract: A computing device to compute clusters using random subsets of variables is provided. Each data point of a plurality of data points is associated with a variable to define a plurality of variables. A subset of the plurality of variables is randomly selected. The subset does not include all of the plurality of variables. A number of clusters into which to segment the received data is determined. Cluster data that defines each cluster of the determined number of clusters is determined by executing a clustering algorithm with the received data using only the plurality of data points defined for each observation that are associated with the randomly selected subset of the plurality of variables. The determined cluster data is stored to cluster second data into the determined number of clusters. The second data is different from the received data.

Abstract: A computing device to compute composite clusters is provided. A first and a second plurality of centroid locations are computed by executing a clustering algorithm with a first portion of data and a first input parameter and a second portion of the data and a second input parameter, respectively. The first portion is different from the second portion or the first input parameter is different from the second input parameter. A plurality of composite centroid locations is computed using the computed first and second plurality of centroid locations to define a composite set of clusters. An observation is selected. A cluster of the composite set of clusters to which to assign the observation is determined using the plurality of composite centroid locations. The selecting and the determining is repeated with each observation of the plurality of observations as the observation to define cluster assignments for the plurality of observations.

Abstract: A computing device to automatically cluster a dataset is provided. Data that includes a plurality of observations with a plurality of data points defined for each observation is received. Each data point of the plurality of data points is associated with a variable to define a plurality of variables. A number of clusters into which to segment the received data is repeatedly selected by repeatedly executing a clustering algorithm with the received data. A plurality of sets of clusters is defined based on the repeated execution of the clustering algorithm that resulted in the selected number of clusters. A plurality of composite clusters is defined based on the defined plurality of sets of clusters. The plurality of observations is assigned to the defined plurality of composite clusters using the plurality of data points defined for each observation.

Abstract: A computing device to automatically cluster a dataset is provided. Data that includes a plurality of observations with a plurality of data points defined for each observation is received. Each data point of the plurality of data points is associated with a variable to define a plurality of variables. A number of clusters into which to segment the received data is repeatedly selected by repeatedly executing a clustering algorithm with the received data. A plurality of sets of clusters is defined based on the repeated execution of the clustering algorithm that resulted in the selected number of clusters. A plurality of composite clusters is defined based on the defined plurality of sets of clusters. The plurality of observations is assigned to the defined plurality of composite clusters using the plurality of data points defined for each observation.