Claims:WE CLAIM:
1. A method for mining data to generate actionable insights, the method comprising:
receiving, via a data mining engine, an input data and a target data from one or more sources;
detecting, via the data mining engine, a defect in the target data using a neural network based predictive model and a scorecard rule table, wherein the scorecard rule table comprises a plurality of scorecards corresponding to a plurality of nodes of the neural network, and wherein each of the plurality of scorecards comprises a plurality of rules corresponding to a plurality of data variables in the input data;
determining, via the data mining engine, at least one root cause for the defect by determining at least one significant scorecard of the plurality of scorecards and at least one significant rule in the at least one significant scorecard that contributed to the detection of the defect; and
generating, via the data mining engine, one or more actionable insights based on the at least one root cause.

2. The method of claim 1, further comprising preparing the input data for data mining.

3. The method of claim 1, wherein the neural network comprises a self-learning neural network comprising at least one hidden layer comprising at least one hidden node.

4. The method of claim 3, wherein each of the plurality of scorecards corresponds to a hidden node in the at least one hidden layer of the neural network.

5. The method of claim 1, wherein the neural network based predictive model comprises:
a linear sum of a plurality of logistic regression models; and
an output layer comprising at least one output node.

6. The method of claim 1, further comprising training the neural network based predictive model using a training data comprising a plurality of training data variables.

7. The method of claim 1, further comprising generating the scorecard rule table by:
generating a score, from the predictive model, for each of a plurality of data elements for each of the plurality of data variables;
determining a score order for each of the plurality of data elements for each of the plurality of data variables based on the corresponding score;
classifying each of the plurality of data variables into one of a binary data variable, a nominal data variable, and a continuous data variable;
categorizing the plurality of data variables based on the classification and the corresponding values; and
generating the scorecard rule table based on the classification, the score order, and the categorization.

8. The method of claim 1, wherein detecting the defect comprises determining if a score for an output parameter of the predictive model is greater than a pre-defined threshold.

9. The method of claim 8, wherein determining the at least one root cause comprises determining the at least one significant scorecard of the plurality of scorecards and the at least one significant rule in the at least one significant scorecard that contributed in the score being greater than the pre-defined threshold.

10. The method of claim 1, wherein the at least one significant scorecard of the plurality of scorecards and the at least one significant rule in the at least one significant scorecard is determined from a plurality of coefficients of the neural network based predictive model.

11. The method of claim 1, wherein the input data comprises a manufacturing operation data, and wherein the defect comprises a component defect in the manufacturing operation.

12. The method of claim 1, wherein the one or more actionable insights comprise one or more recommendations to eradicate the defect or to improve an efficiency.

13. A system for mining data to generate actionable insights, the system comprising:
at least one processor; and
a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving an input data and a target data from one or more sources;
detecting a defect in the target data using a neural network based predictive model and a scorecard rule table, wherein the scorecard rule table comprises a plurality of scorecards corresponding to a plurality of nodes of the neural network, and wherein each of the plurality of scorecards comprises a plurality of rules corresponding to a plurality of data variables in the input data;
determining at least one root cause for the defect by determining at least one significant scorecard of the plurality of scorecards and at least one significant rule in the at least one significant scorecard that contributed to the detection of the defect; and
generating one or more actionable insights based on the at least one root cause.

14. The system of claim 13, wherein the neural network comprises a self-learning neural network comprising at least one hidden layer comprising at least one hidden node, and wherein each of the plurality of scorecards corresponds to a hidden node in the at least one hidden layer of the neural network.

15. The system of claim 13, wherein the neural network based predictive model comprises:
a linear sum of a plurality of logistic regression models; and
an output layer comprising at least one output node.

16. The system of claim 13, wherein the operations further comprise training the neural network based predictive model using a training data comprising a plurality of training data variables.

17. The system of claim 13, wherein the operations further comprise generating the scorecard rule table by:
generating a score, from the predictive model, for each of a plurality of data elements for each of the plurality of data variables;
determining a score order for each of the plurality of data elements for each of the plurality of data variables based on the corresponding score;
classifying each of the plurality of data variables into one of a binary data variable, a nominal data variable, and a continuous data variable;
categorizing the plurality of data variables based on the classification and the corresponding values; and
generating the scorecard rule table based on the classification, the score order, and the categorization.

18. The system of claim 13, wherein detecting the defect comprises determining if a score for an output parameter of the predictive model is greater than a pre-defined threshold, and wherein determining the at least one root cause comprises determining the at least one significant scorecard of the plurality of scorecards and the at least one significant rule in the at least one significant scorecard that contributed in the score being greater than the pre-defined threshold.

19. The system of claim 13, wherein the at least one significant scorecard of the plurality of scorecards and the at least one significant rule in the at least one significant scorecard is determined from a plurality of coefficients of the neural network based predictive model.

Dated this 14th day of March, 2017

R Ramya Rao
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
This disclosure relates generally to data mining, and more particularly to system and method for mining data to generate actionable insights.