## Mining Asynchronous Periodic Patterns in Time Series Data (2000)

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Venue: | Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD |

Citations: | 60 - 8 self |

### BibTeX

@INPROCEEDINGS{Yang00miningasynchronous,

author = {Jiong Yang and Wei Wang and Philip S. Yu},

title = {Mining Asynchronous Periodic Patterns in Time Series Data},

booktitle = {Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD},

year = {2000},

pages = {275--279}

}

### Years of Citing Articles

### OpenURL

### Abstract

Periodicy detection in time series data is a challenging problem of great importance in many applications.

### Citations

2864 | Fast Algorithms for Mining Association Rules - Agrawal, Srikant - 1994 |

1240 | Mining sequential patterns
- Agrawal, Srikant
- 1995
(Show Context)
Citation Context ...ns and extensions of our algorithm in Section 8. Section 9 presents experimental results. The conclusion is drawn in Section 10. 2 Related Work Discovering sequential patterns was first introduced in =-=[2]-=- and [25]. The input data is a set of sequences, called data-sequences. Each data-sequence is a list of transactions. Typically there is a transaction time associated with each transaction. A sequenti... |

588 | Mining sequential patterns: Generalizations and performance improvements
- Srikant, Agrawal
- 1996
(Show Context)
Citation Context ...xtensions of our algorithm in Section 8. Section 9 presents experimental results. The conclusion is drawn in Section 10. 2 Related Work Discovering sequential patterns was first introduced in [2] and =-=[25]-=-. The input data is a set of sequences, called data-sequences. Each data-sequence is a list of transactions. Typically there is a transaction time associated with each transaction. A sequential patter... |

315 | Discovery of frequent episodes in event sequences - Mannila, Toivonen, et al. - 1997 |

215 | Efficient Time Series Matching by Wavelets - Chan, Fu - 1999 |

168 | Programming Pearls - Bentley - 1986 |

139 | Similarity-Based Queries for Time Series Data - Rafiei, Mendelzon - 1997 |

136 | Efficient mining of partial periodic patterns in time series database
- Han, Dong, et al.
- 1999
(Show Context)
Citation Context ...sumed that the disturbance within a series of repetitions of a pattern, if any, would not result in the loss of synchronization of subsequent occurrences of the pattern with previous occurrences [12] =-=[13]. For exam-=-ple, "Joe Smith reads newspaper every morning" is a periodic pattern. Even though Joe might not read newspaper in the morning occasionally, this disturbance will not affect the fact that Joe... |

121 | Mining Association Rules with Multiple Minimum Supports - Liu, Hsu, et al. |

116 | Finding patterns in time series: a dynamic programming approach - Berndt, Cliord - 1996 |

111 | Event detection from time series data - Guralnik, Srivastava - 1999 |

110 | Querying shapes of histories - AGRAWAL, PSAILA, et al. - 1995 |

107 | C.: Efficiently supporting ad hoc queries in large datasets of time sequences - Korn, Jagadish, et al. - 1997 |

104 | A Probabilistic Approach to Fast Pattern Matching in Time Series Databases - Keogh, Smyth - 1997 |

86 | Cyclic Association Rules
- Ozden, Ramaswamy, et al.
- 1998
(Show Context)
Citation Context ... mining algorithm is that it can discover all periodic patterns regardless of the period length. Most previous research in this area focused on patterns for some pre-specified period length [12] [13] =-=[21]-=- or some pre-defined calendar [24]. Unfortunately, in practice, the period is not always available a priori (It is also part of what we want to mine out from the data). The stock of different merchand... |

64 | Efficiently Mining Long Patterns from Databases - Jr - 1998 |

55 | Segment-Wise Periodic Patterns in Time Related Database
- Han, Gong, et al.
- 1998
(Show Context)
Citation Context ...ea assumed that the disturbance within a series of repetitions of a pattern, if any, would not result in the loss of synchronization of subsequent occurrences of the pattern with previous occurrences =-=[12] [13]. For-=- example, "Joe Smith reads newspaper every morning" is a periodic pattern. Even though Joe might not read newspaper in the morning occasionally, this disturbance will not affect the fact tha... |

50 |
Discovering Frequent Event Patterns With Multiple Granularities in Time Sequences
- Bettini, Wang, et al.
- 1998
(Show Context)
Citation Context ...related to partial periodicity such as the Apriori Property and the max-subpattern hit set property. However, the proposed solution requires that the predefined period and the synchronous pattern. In =-=[7]-=-, Bettini et. al. proposed an algorithm to discover temporal patterns in time sequences. The basic components of the algorithm includes timed automata with granularities (TAGs) and a number of heurist... |

40 | Mining deviants in a time series database - Jagadish, Koudas, et al. - 1999 |

38 | Efficient Algorithms for Discovering Frequent Sets - Feldman, Aumann, et al. - 1997 |

37 | Identifying distinctive subsequences in multivariate time series by clustering - Oates - 1999 |

37 | Infominer: Mining surprising periodic patterns
- Yang, Wang, et al.
- 2001
(Show Context)
Citation Context ...th a user-specified minimum support, where the support of a sequential pattern is the percentage of data-sequences that contain the pattern. The surprising sequential pattern discovery is proposed in =-=[28]-=-. In this work, the authors search for the patterns whose occurrence is significantly greater than the expectation. The information gain is used to measure the degree of surprise (or significance) of ... |

36 | Supporting fast search in time series for movement patterns in multiples scales - QU, WANG, et al. - 1998 |

36 | On the discovery of interesting patterns in association rules
- Ramaswamy, Mahajan, et al.
- 1998
(Show Context)
Citation Context ...iscover all periodic patterns regardless of the period length. Most previous research in this area focused on patterns for some pre-specified period length [12] [13] [21] or some pre-defined calendar =-=[24]-=-. Unfortunately, in practice, the period is not always available a priori (It is also part of what we want to mine out from the data). The stock of different merchandises may be replenished at differe... |

23 | Mining Generalized Association rules and Sequential Patterns Using SQL Queries - Thomas, Sarawagi - 1998 |

15 | Querying continuous time sequences - Lin, Risch - 1998 |

8 | Temporal Reasoning with qualitative and quantitative information about points and durations - Wetprasit, Sattar - 1998 |

6 | Rule Discovery from Time - Das, Lin, et al. - 1998 |

2 | Gopal Renganathan, and Padhraic Smyth. Rule discovery from time series - Das, Lin, et al. - 1998 |

1 | Event Detection from Time - Guralnik, Srivastava - 1999 |