Multivariate data analysis method and uses thereof
First Claim
Patent Images
1. A process for multivariate data analysis comprising the steps of:
- using an adjoint matrix to compute a new distance for a data set in a Mahalanobis space; and
determining the relation of a datum to the Mahalanobis space.
0 Assignments
0 Petitions
Accused Products
Abstract
A process involves collecting data relating to a particular condition and parsing the data from an original set of variables into subsets. For each subset defined, Mahalanobis distances are computed for known normal and abnormal values and the square root of these Mahalanobis distances is computed. A multiple Mahalanobis distance is calculated based upon the square root of Mahalanobis distances. Signal to noise ratios are obtained for each run of an orthogonal array in order to identify important subsets. This process has applications in identifying important variables or combinations thereof from a large number of potential contributors to a condition.
18 Citations
16 Claims
-
1. A process for multivariate data analysis comprising the steps of:
-
using an adjoint matrix to compute a new distance for a data set in a Mahalanobis space; and
determining the relation of a datum to the Mahalanobis space. - View Dependent Claims (2, 3, 4, 5, 6)
-
-
7. A multivariable data analysis process comprising the steps of:
-
defining a set of variables relating to a condition;
collecting a data set of the set of variables for a normal group;
computing standardized values of the set of variables of the normal group;
constructing a Mahalanobis space for the normal group;
computing a distance for an abnormal value outside the Mahalanobis space;
identifying important variables from the set of variables using orthogonal arrays and signal to noise ratios; and
monitoring conditions in future based upon the important variables. - View Dependent Claims (8, 9, 10, 11)
-
-
12. A multivariate data analysis process comprising the steps of:
-
defining a plurality of subsets from a set of variables relating to a condition;
calculating Mahalanobis distance for a normal value and an abnormal value for each of said plurality of subsets;
computing a square root of each of the Mahalanobis distances;
computing a multiple Mahalanobis distance from said square roots; and
selecting an important subset based on signal to noise ratios attained for each run of an orthogonal array of said multiple Mahalanobis distances. - View Dependent Claims (13, 14, 15, 16)
-
Specification