A decision tree analysis system and method for navigating a multi-dimensional decision tree uses acceptable alternative child nodes of a target child node to select an end child node for a parent node, where the parent node and the final child node define a single step of a navigation path for the multi-dimensional decision tree. The acceptable alternative child nodes are based on an acceptance delta parameter for a particular attribute, which defines a value range about an attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for navigating a multi-dimensional decision tree, the method comprising: determining a target child node from a plurality of child nodes of a parent node on the multi-dimensional decision tree based on a first attribute value of each of the child nodes; determining any acceptable alternative child node of the target child node from the plurality of child nodes based on an acceptance delta parameter for the first attribute value, the acceptance delta parameter defining a value range about the first attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node; and selecting an end child node from the target child node and any acceptable alternative child node based on a second attribute value of each of the target child node and any acceptable alternative child node, wherein the parent node and the end child node define a single step of a navigation path for the multi-dimensional decision tree. 2. The method of claim 1, wherein an attribute corresponding to the second attribute value is the last attribute of multiple attributes for each of the child nodes. 3. The method of claim 2, further comprising setting a processing order of the multiple attributes for each of the child nodes so that one of the multiple attributes is designated as an attribute corresponding to the first attribute value and another attribute of the multiple attributes is designated as the attribute corresponding to the second attribute value. 4. The method of claim 2, wherein the step of determining the target child node and the step of determining any acceptable alternative child node of the target child node are repeated for each of the multiple attributes except for the last attribute to select the end child node. 5. The method of claim 4, wherein the step of determining the target child node and the step of determining any acceptable alternative child node of the target child node that are repeated for each of the multiple attributes includes using different acceptance parameters. 6. The method of claim 1, wherein the determining of the target child node includes selecting one of the child nodes having the highest first attribute value among the child nodes. 7. The method of claim 6, wherein the selecting of the end child node includes selecting one of the target child node and any acceptable alternative child node having the highest second attribute value among the target child node and any acceptable alternative child node. 8. The method of claim 1, wherein each of the child nodes represent a unique state of a cluster of host computers with virtual machines running on at least some of the host computers that can be achieved from a state of the cluster of host computers represented by the parent node. 9. The method of claim 8, wherein the unique state of the cluster of host computers is a result of one or more of the virtual machines migrated from a source host computer to a destination host computer. 10. A computer-readable storage medium containing program instructions for method for navigating a multi-dimensional decision tree, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform steps comprising: determining a target child node from a plurality of child nodes of a parent node on the multi-dimensional decision tree based on a first attribute value of each of the child nodes; determining any acceptable alternative child node of the target child node from the plurality of child nodes based on an acceptance delta parameter for the first attribute value, the acceptance delta parameter defining a value range about the first attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node; and selecting an end child node from the target child node and any acceptable alternative child node based on a second attribute value of each of the target child node and any acceptable alternative child node, wherein the parent node and the end child node define a single step of a navigation path for the multi-dimensional decision tree. 11. The computer-readable storage medium of claim 10, wherein an attribute corresponding to the second attribute value is the last attribute of multiple attributes for each of the child nodes. 12. The computer-readable storage medium of claim 11, where the steps further comprise setting a processing order of the multiple attributes for each of the child nodes so that one of the multiple attributes is designated as an attribute corresponding to the first attribute value and another attribute of the multiple attributes is designated as the attribute corresponding to the second attribute value. 13. The computer-readable storage medium of claim 11, wherein the step of determining the target child node and the step of determining any acceptable alternative child node of the target child node are repeated for each of the multiple attributes except for the last attribute to select the end child node. 14. The computer-readable storage medium of claim 13, wherein the step of determining the target child node and the step of determining any acceptable alternative child node of the target child node that are repeated for each of the multiple attributes includes using different acceptance parameters. 15. The computer-readable storage medium of claim 10, wherein the determining of the target child node includes selecting one of the child nodes having the highest first attribute value among the child nodes. 16. The computer-readable storage medium of claim 15, wherein the selecting of the end child node includes selecting one of the target child node and any acceptable alternative child node having the highest second attribute value among the target child node and any acceptable alternative child node. 17. The computer-readable storage medium of claim 10, wherein each of the child nodes represent a unique state of a cluster of host computers with virtual machines running on at least some of the host computers that can be achieved from a state of the cluster of host computers represented by the parent node. 18. The computer-readable storage medium of claim 17, wherein the unique state of the cluster of host computers is a result of one or more of the virtual machines migrated from a source host computer to a destination host computer. 19. A decision tree analysis system comprising: an interface unit configured to receive a request for a decision tree analysis of a multi-dimensional decision tree; and a decision engine configured to: determine a target child node from a plurality of child nodes of a parent node on the multi-dimensional decision tree based on a first attribute value of each of the child nodes; determine any acceptable alternative child node of the target child node from the plurality of child nodes based on an acceptance delta parameter for the first attribute value, the acceptance delta parameter defining a value range about the first attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node; and select an end child node from the target child node and any acceptable alternative child node based on a second attribute value of each of the target child node and any acceptable alternative child node, wherein the parent node and the end child node define a single step of a navigation path for the multi-dimensional decision tree. 20. The decision tree analysis system of claim 19, wherein an attribute corresponding to the second attribute value is the last attribute of multiple attributes for each of the child nodes. 21. The decision tree analysis system of claim 20, wherein the decision engine is configured to repeatedly determine the target child node and any acceptable alternative child node of the target child node for each of the multiple attributes except for the last attribute to select the end child node. 22. The decision tree analysis system of claim 21, wherein the decision engine is configured to repeatedly determine the target child node and any acceptable alternative child node of the target child node for each of the multiple attributes using different acceptance parameters. 23. The decision tree analysis system of claim 19, wherein the decision engine is configured to determine the target child node by selecting one of the child nodes having the highest first attribute value among the child nodes. 24. The decision tree analysis system of claim 19, wherein the decision engine is configured to select one of the target child node and any acceptable alternative child node having the highest second attribute value among the target child node and any acceptable alternative child node to select the end child node.