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A frequent sequence and pattern mining technology, applied in character and pattern recognition, special data processing applications, instruments, etc.
Pending Publication Date: 2020-11-13
HEBEI UNIV OF TECH
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[0029] The technical problem to be solved by the present invention is to provide a non-overlapping condition adaptive frequent sequence pattern mining method, the method uses the pattern growth strategy to reduce the generation of candidate patterns, and builds a single branch network tree through the depth-first mining strategy to improve support calculation efficiency, and then solve the problem of non-overlapping adaptive frequent sequential pattern mining
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Embodiment 1
[0122] Given a symbolized time series database D={s 1 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 the s 8 =AHAHYCAH,s 2 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 =GCHAGHA}, given the minimum support threshold minsup=3.
[0123] The first step is to read the sequence database D and the minimum support threshold minsup:
[0124] Read in the given sequence database D and the minimum support threshold minsup to obtain the character set E and the number N of sequences in the sequence database D, each of which is recorded as sequence s 1 , sequence s 2 , ..., sequence s k , ..., sequence s N , where 1≤k≤N, the sequence s k Each character in is denoted as the character s 1 , character s 2 , ..., character s j , ..., character s n , where 1≤j≤n;
[0125] Read into the given sequence database D={s 1 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 the s 8 =AHAHYCAH,s 2 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7...
Embodiment 2
[0211] Given a symbolized stock sequence database D={s 1 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 the s 8 =FQFAQYCF,s 2 =s 1 the s 2 the s 3 the s 4 the s 5 the s 6 the s 7 the s 8 the s 9 the s 10 =GCDAQFGQCF}, the expected number of patterns K=3.
[0212] In addition to "the first step, read in the sequence database D and the minimum support threshold minsup; the second step, calculate the candidate pattern set C 1 The support degree of each candidate pattern in , and the pattern whose support degree is greater than or equal to the minimum support threshold minsup is added to the non-overlapping adaptive frequent sequence pattern set F with pattern length 1 1 In the fourth step, when the candidate pattern set C l+1 When it is empty, the mining ends and the sixth step is executed; otherwise, the candidate pattern set C is traversed l+1 , take out the pattern p in turn h and calculate the mode p h The pattern support in the sequence database ...
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Abstract
The invention relates to a non-overlapping adaptive frequent sequence pattern mining method, and belongs to the field of sequence pattern analysis of data mining. According to the method, candidate mode generation is reduced by using a mode growth strategy, and a single-branch network tree is constructed through a depth-first strategy to improve the support degree calculation efficiency; the problem of non-overlapping adaptive frequent sequence pattern mining is solved; according to the method, under the condition that gap constraints are not given, the non-overlapping frequent pattern miningis realized; the problem that the existing sequence pattern mining technology is difficult to consider the flexibility, high efficiency and completeness of mining at the same time is solved; the method is convenient for a user to use, the time complexity and the space complexity can be effectively reduced, and complete and valuable information is obtained while the non-redundancy of a mining result is ensured.
Description
technical field [0001] The technical solution of the invention relates to the field of sequence pattern analysis, in particular to a frequent sequence pattern mining method with adaptive gaps under the condition of no overlap. Background technique [0002] Time series data is a common and important data. It is a numerical sequence arranged in chronological order and widely exists in human production and life, such as passenger flow analysis, marketing, river flow, weather temperature / humidity, stock market Stock prices, heart / EEG, etc. These time series data contain a large amount of potentially valuable information, which can reveal different characteristics of the dynamic system and help users make decision analysis and future trend judgment. However, due to the volatility, variability and uncertainty of time series, it is an important challenge to extract effective information directly from time series data. Therefore, it is necessary to convert it into a character seque...
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