Abstract: A method is provided for identifying partial periodic patterns in an event sequence, wherein pattern includes a list of events from the event sequence. At least one subsequence of the event sequence and at least pattern in the at least one subsequence is identified, such that the at least one pattern exceeds a minimum number of repetitions in the at least one subsequence, and a distance between successive repetitions of the at least one pattern in the at least one subsequence does not exceed a predefined distance threshold. At least one of the at least one pattern and the at least one subsequence is stored.

Abstract: A system and method for determining patterns in a data sequence constructs a compatibility matrix which provides a probability between an actual occurrence of an item and an observed occurrence of that or another item between each item in the data sequence. Candidate patterns are generated. The candidate patterns include items in the data sequence. The candidate patterns are checked against the data sequence to determine a match value based on the compatibility matrix, and significant matches are determined based on candidate patterns having the match value above a threshold.

Abstract: A method is provided for mining partial periodic patterns in an event sequence, wherein each pattern includes a list of events from the event sequence. At least one subsequence of the event sequence and at least one pattern in the at least one subsequence are identified, such that an information gain of the at least one subsequence with respect to the at least one pattern exceeds a predefined threshold. At least one of the at least one pattern and the at least one subsequence is stored.

Abstract: A system and method are provided for discovering significant patterns from a list of records in a dataset. Each record includes a set of items, and each significant pattern includes a subset of items such that a significance of the pattern exceeds a significance level. A significance is computed for each item in the list of records to determine significant items. The records are randomly sampled to select a sample portion of the records. Ambiguous patterns are identified against the sample portion of the records and verified against the entire list of records in the dataset.

Abstract: A system and method for determining patterns in a data sequence constructs a compatibility matrix which provides a probability between an actual occurrence of an item and an observed occurrence of that or another item between each item in the data sequence. Candidate patterns are generated. The candidate patterns include items in the data sequence. The candidate patterns are checked against the data sequence to determine a match value based on the compatibility matrix, and significant matches are determined based on candidate patterns having the match value above a threshold.

Abstract: Periodic patterns in time series data can be hierarchical in nature, where a higher level pattern may comprise repetitions of lower level patterns. In the presence of noises, these repetitions of lower level patterns may not be perfect. A novel model, namely a meta-pattern, is provided in accordance with the present invention to capture these higher level patterns. The meta-pattern can not only provide a more compact representation of patterns but also capture the regularities of pattern evolutions, which may not be expressed by previous models due to the presence of noise. A method is provided to mine meta-patterns in an iterative manner by discovering meta-patterns and their supporting subsequences in the form of lists of segments of contiguous repetitions of a meta-pattern. The number of pattern repetitions in each said segment is at least a predefined threshold min_rep and the distance between any two adjacent segments is at most a predefined threshold max_dis.

Abstract: The traditional association rule problem is extended by allowing a weight to be associate with each item in a transaction to reflect interest/intensity of each item within the transaction. The weighted association rules from a set of tuple lists are discovered, where each tuple consists of an item and an associated weight and each tuple list consists multiple tuples. The weighted association rules (WARs) are generated where some subset of items forms the consequent part of the rule (i.e., right hand side of the rule) and some other (non-overlapped) subset of items from the antecedent part of the rule (i.e., left hand side of the rule).