System and method for temporal data mining
First Claim
1. A method for temporal data mining, comprising:
- receiving as input a temporal data series and a threshold frequency;
finding all frequent episodes of a particular length in the temporal data series;
in successive passes through the temporal data series;
incrementing the particular length to generate an increased length;
combining frequent episodes to create combined episodes of the increased length;
creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes;
identifying one or more occurrences of a candidate episode in the temporal data series;
incrementing a count for each identified occurrence;
determining frequent episodes of the increased length; and
setting the particular length to the increased length; and
producing an output for frequent episodes;
wherein a frequent episode is an episode whose count of occurrences results in a frequency meeting or exceeding the threshold frequency.
12 Assignments
0 Petitions
Accused Products
Abstract
A method, system, and apparatus for temporal data mining is disclosed. The method includes receiving as input a temporal data series comprising time-stamped events, and a threshold frequency. An aspect of this technology is the defining of appropriate frequency counts for non-overlapping and non-interleaved episodes. Two frequency measures and embodiments for obtaining frequent episodes are described. The method includes finding all frequent episodes of a particular length in the temporal data series. The method includes steps executed in successive passes through the temporal data series. The steps include incrementing the particular length to generate an increased length, combining frequent episodes to create combined episodes of the increased length, creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes, identifying one or more occurrences of a candidate episode in the temporal data series, incrementing a count for each identified occurrence, determining frequent episodes of the increased length, and setting the particular length to the increased length.
-
Citations
20 Claims
-
1. A method for temporal data mining, comprising:
-
receiving as input a temporal data series and a threshold frequency;
finding all frequent episodes of a particular length in the temporal data series;
in successive passes through the temporal data series;
incrementing the particular length to generate an increased length;
combining frequent episodes to create combined episodes of the increased length;
creating a set of candidate episodes from the combined episodes by removing combined episodes which have non-frequent sub-episodes;
identifying one or more occurrences of a candidate episode in the temporal data series;
incrementing a count for each identified occurrence;
determining frequent episodes of the increased length; and
setting the particular length to the increased length; and
producing an output for frequent episodes;
wherein a frequent episode is an episode whose count of occurrences results in a frequency meeting or exceeding the threshold frequency. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A system for temporal data mining, comprising:
-
an input module for receiving a temporal data series and a threshold frequency;
a candidate identification and tracking module for identifying one or more occurrences in the temporal data series of a candidate episode and for incrementing a count for each identified occurrence; and
an output module for producing an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency. - View Dependent Claims (9, 10, 11, 12, 13, 14)
-
-
15. An apparatus for temporal data mining, comprising:
-
a processor for executing instructions;
a memory device including instructions comprising;
input instructions for receiving a temporal data series and a threshold frequency;
candidate identification and tracking instructions for identifying one or more occurrences in the temporal data series of a candidate episode and for incrementing a count for each identified occurrence; and
output instructions for producing an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency. - View Dependent Claims (16, 17, 18, 19, 20)
-
Specification