Abstract: Estimating a Bayes factor is provided. Table dimensions of a contingency table are determined. A statistical model type to apply to the contingency table is determined. Fixed marginal totals are specified for either rows or columns when a Multinomial sampling model is applied. A table total is computed when a Poisson sampling model is applied or fixed marginal totals are computed when the Multinomial sampling model is applied to a two by two contingency table. The table total is compared to a first threshold when the Poisson sampling model is applied or fixed marginal totals are compared to a second threshold when the Multinomial sampling model is applied to a two by two contingency table. An estimation method is selected to apply to the contingency table to compute the Bayes factor based on table dimensions, sampling model applied, and fixed marginal totals of the contingency table.

Abstract: In each iteration of the process of mining association rules from transaction data by a cluster of computing systems, each mapper node in the cluster receives a split of the transaction data. Each mapper node scans the split to count an absolute support value of each candidate itemset for current search level(s), and passes the candidate itemsets and their support values to reducer nodes in the cluster. The number of reducer nodes will be determined adaptively based on the number of the candidate itemsets and the number of maximum available resource nodes in the cluster. Each reducer node combines the absolute support value of each candidate itemset, and finds frequent itemsets among them using a minimum support threshold. For each frequent itemset it finds, the reducer node creates association rule(s) satisfying a minimum confidence threshold, and exports all discovered frequent itemsets and association rules to a file system for storage.

Abstract: In each iteration of the process of mining association rules from transaction data by a cluster of computing systems, each mapper node in the cluster receives a split of the transaction data. Each mapper node scans the split to count an absolute support value of each candidate itemset for current search level(s), and passes the candidate itemsets and their support values to reducer nodes in the cluster. The number of reducer nodes will be determined adaptively based on the number of the candidate itemsets and the number of maximum available resource nodes in the cluster. Each reducer node combines the absolute support value of each candidate itemset, and finds frequent itemsets among them using a minimum support threshold. For each frequent itemset it finds, the reducer node creates association rule(s) satisfying a minimum confidence threshold, and exports all discovered frequent itemsets and association rules to a file system for storage.

Abstract: One or more processors initiate cluster feature (CF)-tree based hierarchical clustering on leaf entries of CF-trees included in a plurality of subsets. One or more processors, generate respective partial clustering solutions for the subsets. A partial clustering solution includes a set of regular sub-clusters and candidate outlier sub-clusters. One or more processors generate initial regular clusters by performing hierarchical clustering using the regular sub-clusters. For a candidate outlier sub-cluster, one or more processors determine a closest initial regular cluster, and a distance separating the candidate outlier sub-cluster and the closest initial regular cluster. One or more processors determine which candidate outlier sub-clusters are outlier clusters based on which candidate outlier sub-clusters have a computed distance to their respective closest initial regular cluster that is greater than a corresponding distance threshold associated with their respective closest initial regular cluster.

Abstract: Embodiments of the present invention provide efficient systems and methods for processing large data sets using a composite function. Embodiments of the present invention can be used to compute a broad range of composite functions in a single map-reduce job. Each mapper computes an additive function G on a set of specified data partitions, and then passes the results to one or more reducers. The one or more reducers can then compute a function F, using the aggregate results of function G and data from a single partition.

Abstract: Embodiments of the present invention provide efficient systems and methods for processing large data sets using a composite function. Embodiments of the present invention can be used to compute a broad range of composite functions in a single map-reduce job. Each mapper computes an additive function G on a set of specified data partitions, and then passes the results to one or more reducers. The one or more reducers can then compute a function F, using the aggregate results of function G and data from a single partition.

Abstract: One or more processors initiate cluster feature (CF)-tree based hierarchical clustering on leaf entries of CF-trees included in a plurality of subsets. One or more processors, generate respective partial clustering solutions for the subsets. A partial clustering solution includes a set of regular sub-clusters and candidate outlier sub-clusters. One or more processors generate initial regular clusters by performing hierarchical clustering using the regular sub-clusters. For a candidate outlier sub-cluster, one or more processors determine a closest initial regular cluster, and a distance separating the candidate outlier sub-cluster and the closest initial regular cluster. One or more processors determine which candidate outlier sub-clusters are outlier clusters based on which candidate outlier sub-clusters have a computed distance to their respective closest initial regular cluster that is greater than a corresponding distance threshold associated with their respective closest initial regular cluster.

Abstract: In each iteration of the process of mining association rules from transaction data by a cluster of computing systems, each mapper node in the cluster receives a split of the transaction data. Each mapper node scans the split to count an absolute support value of each candidate itemset for current search level(s), and passes the candidate itemsets and their support values to reducer nodes in the cluster. The number of reducer nodes will be determined adaptively based on the number of the candidate itemsets and the number of maximum available resource nodes in the cluster. Each reducer node combines the absolute support value of each candidate itemset, and finds frequent itemsets among them using a minimum support threshold. For each frequent itemset it finds, the reducer node creates association rule(s) satisfying a minimum confidence threshold, and exports all discovered frequent itemsets and association rules to a file system for storage.

Abstract: In each iteration of the process of mining association rules from transaction data by a cluster of computing systems, each mapper node in the cluster receives a split of the transaction data. Each mapper node scans the split to count an absolute support value of each candidate itemset for current search level(s), and passes the candidate itemsets and their support values to reducer nodes in the cluster. The number of reducer nodes will be determined adaptively based on the number of the candidate itemsets and the number of maximum available resource nodes in the cluster. Each reducer node combines the absolute support value of each candidate itemset, and finds frequent itemsets among them using a minimum support threshold. For each frequent itemset it finds, the reducer node creates association rule(s) satisfying a minimum confidence threshold, and exports all discovered frequent itemsets and association rules to a file system for storage.

Abstract: One or more processors initiate cluster feature (CF)-tree based hierarchical clustering on leaf entries of CF-trees included in a plurality of subsets. One or more processors, generate respective partial clustering solutions for the subsets. A partial clustering solution includes a set of regular sub-clusters and candidate outlier sub-clusters. One or more processors generate initial regular clusters by performing hierarchical clustering using the regular sub-clusters. For a candidate outlier sub-cluster, one or more processors determine a closest initial regular cluster, and a distance separating the candidate outlier sub-cluster and the closest initial regular cluster. One or more processors determine which candidate outlier sub-clusters are outlier clusters based on which candidate outlier sub-clusters have a computed distance to their respective closest initial regular cluster that is greater than a corresponding distance threshold associated with their respective closest initial regular cluster.

Abstract: One or more processors initiate cluster feature (CF)-tree based hierarchical clustering on leaf entries of CF-trees included in a plurality of subsets. One or more processors, generate respective partial clustering solutions for the subsets. A partial clustering solution includes a set of regular sub-clusters and candidate outlier sub-clusters. One or more processors generate initial regular clusters by performing hierarchical clustering using the regular sub-clusters. For a candidate outlier sub-cluster, one or more processors determine a closest initial regular cluster, and a distance separating the candidate outlier sub-cluster and the closest initial regular cluster. One or more processors determine which candidate outlier sub-clusters are outlier clusters based on which candidate outlier sub-clusters have a computed distance to their respective closest initial regular cluster that is greater than a corresponding distance threshold associated with their respective closest initial regular cluster.