This disclosure provides a computer-program product, system, method and apparatus for accessing a representation of a category or item and accessing a set of multiple transactions. The transactions are processed to identify items found amongst the transactions, and the items are ordered based on an information-gain heuristic. A depth-first search for a group of best association rules is then conducted using a best-first heuristic and constraints that make the search efficient. The best rules found during the search can then be displayed to a user, along with accompanying statistics. The user can then select rules that appear to be most relevant, and further analytics can be applied to the selected rules to obtain further information about the information provided by these rules.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-program product tangibly embodied in a non-transitory computer-readable storage medium having instructions stored thereon, the instructions executable to cause a data processing apparatus to perform operations including: accessing a representation of a category or item; accessing a set of multiple transactions (hereinafter “set A”), each of the transactions including a label indicating whether or not the transaction is a member of that category or contains that item; compiling a list of distinct items found in the transactions of set A; computing an information gain metric, wherein each of the information gain metrics represent mutual information of the respective distinct item and the target category or item within set A; ordering the distinct items on the list based on the information gain metrics; and evaluating usefulness of multiple association rules in detection of the category or item within transactions, wherein each of the association rules includes a logical conjunction of the presence or absence of one or more items on the list, wherein evaluating the usefulness of the multiple association rules includes applying each of the association rules to each of the text documents of set A. 2. The computer-program product of claim 1, wherein applying each of the association rules to each of the transactions of set A includes: counting a number (b) of the transactions in set A that are associated with a true-positive application of the association rule, wherein a transaction is associated with a true-positive application of the association rule when the transaction: includes the presence of all items in that rule that are supposed to be present; excludes all items in that rule that are supposed to be absent and either contains the item of interest in the transaction, or is a member of the target category; counting a number (c) of the text documents in set A that are associated with a false-positive application of the association rule, wherein a document is associated with a false-positive application of the association rule when the document: includes the presence of all items in that rule that are supposed to be present; excludes all items in that rule that are supposed to be absent and either does not contain the item of interest in the transaction, or is not a member of the target category. 3. The computer-program product of claim 2, wherein applying each of the association rules to each of the text transactions further includes: counting a number (d) of the text transactions in set A that are associated with a true-negative application of the association rule, wherein a transaction is associated with a true-negative application of the association rule when the transaction: does not contain at least one item in that rule that is supposed to be present or contains at least one item in that rule that is supposed to be absent and does not contain the item of interest in the transaction, or is not a member of the target category; counting a number (e) of the transactions in set A that are associated with a false-negative application of the association rule, wherein a transaction is associated with a false-negative application of the association rule when the transaction: does not contain at least one item in that rule that is supposed to be present or contains at least one item in that rule that is supposed to be absent and either contains the item of interest in the transaction, or is a member of the target category; omits at least one of the at least two distinct words of the logical conjunction; and does not address the topic; 4. The computer-program product of claim 3, wherein, with respect to each of the multiple association rules, evaluating the usefulness further includes computing a usefulness metric as a function of b, c, d and e. 5. The computer-program product of claim 1, wherein the operations further include: computing a statistical significance metric with respect to each of the distinct items, wherein each of the statistical significance metrics computed with respect to a distinct item indicates statistical significance of the information gain metric computed with respect to the distinct item; accessing a statistical significance threshold parameter; and removing at least one of the distinct items from the list prior to applying each of the association rules to each of the text documents of set A, wherein each of the at least one distinct item removed from the list is removed based on its respective statistical significance metric being less than the statistical significance threshold. 6. The computer-program product of claim 1, wherein the operations further include: accessing a parameter (k), wherein k is an integer; accessing a rule expansion parameter (f), wherein f is an integer; and defining a top-k set of association rules that is empty upon being defined and is operable for referencing k association rules found to be most useful in the classification while evaluation of the usefulness of the multiple association rules progresses. 7. The computer-program product of claim 6, wherein the multiple association rules include f initial association rules, and wherein evaluating usefulness of multiple association rules further includes: selecting a first one of the distinct items on the list, wherein the first one of the distinct items is selected based on the information gain metric computed with respect to the first one of the distinct items being higher than all other of the information gain metrics computed with respect to distinct items on the list; forming the f initial association rules such that the f initial association rules collectively include f+1 distinct items on the list, wherein each of the f initial association rules is a logical conjunction of items comprising: the first one of the distinct items on the list; and another one of the distinct items on the list; and removing the f+1 distinct items from the list. 8. The computer-program product of claim 7, wherein evaluating usefulness of multiple association rules further includes: identifying k association rules from amongst the f initial association rules, wherein the usefulness metrics computed with respect to the k identified association rules are higher than the usefulness metrics computed with respect to the initial association rules not selected. 9. The computer-program product of claim 8, wherein evaluating usefulness of multiple association rules further includes: adding the k identified association rules to the top-k set of association rules. 10. The computer-program product of claim 7, wherein evaluating usefulness of multiple association rules further includes: identifying improvable association rules from amongst the f initial association rules; subsequent to removing the f+1 items from the list, generating f second-stage association rules with respect to each of the improvable association rules, wherein each of the second-stage association rules generated with respect to an improvable association rule is a logical conjunction that includes: the two items included in the improvable association rule; and a distinct item remaining on the list after the f+1 distinct items are removed from the list.