A computer-implemented method for summarizing multi-sensor data comprising: computing a plurality of histograms from sensor data associated with a plurality of sensors; clustering, from the plurality of histograms, respective histograms of each of the plurality of sensors to obtain a first plurality of sensor-clusters based on shape of the respective histograms, each sensor-cluster of the first plurality of sensor-clusters comprising a centroid histogram representative of distinct sensor behavior for a distinct sensor of the plurality of sensors; performing frequent pattern mining on the first plurality of sensor-clusters to extract a first set of rules, a rule of the first set of rules being associated with a set of sensors of the plurality of sensors and comprising a set of sensor-clusters occurring frequently in the first plurality of sensor-clusters over a time period; merging selectively two or more sensor-clusters from amongst the first plurality of sensor-clusters to obtain a second plurality of sensor-clusters, the two or more sensor-clusters selected corresponding to a sensor of the set of sensors, the two or more sensor-clusters being merged based on two or more rules from amongst the first set of rules associated with the two or more sensor-clusters and a distance measure between the two or more sensor-clusters of the sensor; extracting a second set of rules from the second plurality of sensor-clusters, the second set of rules indicative of distinct sensor behaviors associated with the second plurality of sensor-clusters; identifying a plurality of sets of correlated sensors from the second plurality of sensor-clusters based on the second set of rules; and extracting a third set of rules from the one or more sets of correlated sensors, the third set of rules summarizing the multi-sensor data to represent prominent co-occurring sensor behaviors. computing a plurality of histograms from sensor data associated with a plurality of sensors; clustering, from the plurality of histograms, respective histograms of each of the plurality of sensors to obtain a first plurality of sensor-clusters based on shape of the respective histograms, each sensor-cluster of the first plurality of sensor-clusters comprising a centroid histogram representative of distinct sensor behavior for a distinct sensor of the plurality of sensors; performing frequent pattern mining on the first plurality of sensor-clusters to extract a first set of rules, a rule of the first set of rules being associated with a set of sensors of the plurality of sensors and comprising a set of sensor-clusters occurring frequently in the first plurality of sensor-clusters over a time period; merging selectively two or more sensor-clusters from amongst the first plurality of sensor-clusters to obtain a second plurality of sensor-clusters, the two or more sensor-clusters selected corresponding to a sensor of the set of sensors, the two or more sensor-clusters being merged based on two or more rules from amongst the first set of rules associated with the two or more sensor-clusters and a distance measure between the two or more sensor-clusters of the sensor; extracting a second set of rules from the second plurality of sensor-clusters, the second set of rules indicative of distinct sensor behaviors associated with the second plurality of sensor-clusters; identifying a plurality of sets of correlated sensors from the second plurality of sensor-clusters based on the second set of rules; and extracting a third set of rules from the one or more sets of correlated sensors, the third set of rules summarizing the multi-sensor data to represent prominent co-occurring sensor behaviors.