Method for mining causality rules with applications to electronic commerce
First Claim
1. A method for a computer to derive category information from a database of event data, wherein an event selected from said event data may belong to one or more of a plurality of categories, comprising the steps of:
- preprocessing said event data into an event category database by ordering said event data into at least one event category sequence; and
determining at least one causality rule from said preprocessed event data, wherein said determining of causality rules comprises the steps of;
(a) selecting at least one trigger event category from said preprocessed event database;
(b) determining a first consequential set comprising at least one event category from said preprocessed event database which may be caused by said at least one trigger event category;
(c) pairing said at least one trigger event category and said first consequential set into a first causality rule candidate;
(d) counting the number of occurrences of said first causality rule candidate in said preprocessed event database;
(e) generating a successive causality rule candidate comprising said at least one trigger event category and a successive consequential set by adding at least one additional event category which may be caused by said trigger event category;
(f) obtaining a count of the number of occurrences of said successive causality rule candidate;
(g) comparing said count of the number of occurrences of said successive causality rule candidate to a pre-set threshold; and
(h) repeating steps (e) through (g) if said count exceeds said pre-set threshold.
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Abstract
For mining causality rules in an event database, the rules are obtained by iteratively generating candidate rules and counting their occurrences in the event database. Newly identified causality rules are used to generate the next set of candidate rules to be evaluated, by increasing the size of the set of consequential events triggered by triggering events and/or the number of triggering events. The preferred embodiment uses an iterative approach to deriving the causality rules in order of the consequential set sizes and triggering set sizes. The detection of an occurrence of a causality rule in an event sequence is handled as a sub-sequence matching problem using a novel hierarchical matching method to improve efficiency.
49 Citations
11 Claims
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1. A method for a computer to derive category information from a database of event data, wherein an event selected from said event data may belong to one or more of a plurality of categories, comprising the steps of:
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preprocessing said event data into an event category database by ordering said event data into at least one event category sequence; and determining at least one causality rule from said preprocessed event data, wherein said determining of causality rules comprises the steps of; (a) selecting at least one trigger event category from said preprocessed event database; (b) determining a first consequential set comprising at least one event category from said preprocessed event database which may be caused by said at least one trigger event category; (c) pairing said at least one trigger event category and said first consequential set into a first causality rule candidate; (d) counting the number of occurrences of said first causality rule candidate in said preprocessed event database; (e) generating a successive causality rule candidate comprising said at least one trigger event category and a successive consequential set by adding at least one additional event category which may be caused by said trigger event category; (f) obtaining a count of the number of occurrences of said successive causality rule candidate; (g) comparing said count of the number of occurrences of said successive causality rule candidate to a pre-set threshold; and (h) repeating steps (e) through (g) if said count exceeds said pre-set threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for a computer to derive category information from a database of event data, wherein an event selected from said event data may belong to one or more of a plurality of categories, comprising the steps of:
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(a) preprocessing said event data into an event category database by sorting said event data according to event criteria and mapping said sorted event data into said event categories; and (b) determining at least one causality rule from said preprocessed event data. - View Dependent Claims (10, 11)
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Specification