A novel approach for mining cyclically repeated patterns with multiple minimum supports

Hu, Ya-Han; Tsai, Chih‐Fong; Tai, Chun-Tien; Chiang, In-Chi

doi:10.1016/j.asoc.2014.10.047

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…More recent data mining techniques find “partial periodic patterns” that occur repeatedly in time series, sometimes with imperfect regularity [ 5 , 6 , 12 , 25 , 27 – 29 , 32 , 34 , 39 , 40 , 61 , 62 ]. These methods represent discrete time series as strings and search for recurring patterns.…”

Section: Related Workmentioning

confidence: 99%

Modeling Individual Cyclic Variation in Human Behavior

Pierson

Althoff

Leskovec

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets—of human menstrual cycle symptoms and physical activity tracking data—yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

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Section: Related Workmentioning

confidence: 99%

Modeling Individual Cyclic Variation in Human Behavior

Pierson

Althoff

Leskovec

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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“…Ya-hanhu, et al [13], has proposed cyclically repeated patterns (CRP) mining, in which a new parameter called repetition support, is considered in the mining process. In the paper, they have first redefined CRPs based on the MIR and original form of the sequence minimum support.…”

Section: Literature Surveymentioning

confidence: 99%

Cyclic Repeated Patterns in Sequential Pattern Mining Based on the Fuzzy C-Means Clustering and Association Rule Mining Technique

Selvanambi¹,

Jaisankar²

2017

IJIES

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Abstract:The main aim of the proposed method is to remove cyclic repeated patterns in sequential pattern mining. Initially the input dataset is fed to the clustering process, in which fuzzy c means clustering algorithm is used to cluster the available data based on the similar sequential pattern. This approach is able to mine the patterns with the help of association rule mining, here two major tasks are present one is frequent item set generation and rule generation. In frequent item set generation, support and confidence value is evaluated for each pattern. Based on that, the rules are generated in rule generation. After the mining process, the threshold is fixed and based on that repeated cyclic patterns are removed and stored to the database. The performance of the suggested method is evaluated by means of execution time, memory and database difference ratio. The implementation is done with the help of JAVA platform.

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