Abstract: The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.

Abstract: The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.

Abstract: Systems, methods, and computer-readable code stored on a non-transitory media for mining association rules include determining a minimum support threshold and a minimum confidence threshold for association rule mining; determining a sampling model; sampling transactions from a transaction dataset; mining association rules from the sampled transactions; and transmitting mined association rules.

Abstract: Systems, methods, and computer-readable code stored on a non-transitory media for mining association rules include determining a minimum support threshold and a minimum confidence threshold for association rule mining; determining a sampling model; sampling transactions from a transaction dataset; mining association rules from the sampled transactions; and transmitting mined association rules.

Abstract: A system and method for logically masking data by implementing masking algorithms is provided. The method includes receiving one or more inputs from user regarding type of data masking to be implemented depending on type of data entry. Data entries include alphabetical data, data comprising unique codes, data comprising dates and numerical data. Based on inputs received, the data entries are classified and appropriate masking algorithms are executed. For masking numerical data entries, the data entries are first grouped using clustering algorithms and are then shuffled using shuffling algorithms. For low level of data masking selected by a user, numerical data entries are shuffled within groups and for high level of data masking selected by a user, numerical data entries are shuffled across groups.