Skip to main content
SpringerLink
Log in

Discretized sequential pattern mining for behaviour classification

  • Original Paper
  • Published:
Granular Computing Aims and scope Submit manuscript

Abstract

This paper proposes an approach for behavioural analysis on continuous data based on sequential pattern mining (SPM) and classification. Discretization and granularity issues associated with SPM and classification with time series data are examined, and a thorough examination of the many factors and parameters that are used to control the granularity of the various dimensions of the problem is conducted. The key contribution of the paper is the seminal examination of machine learning-based classification driven by sequential pattern mining to classify behaviour patterns. Results are reported on the effectiveness of various levels and settings on acceleration data collected from the walking activity of 17 subjects obtained from a publicly available dataset. Using the optimal set of parameter settings identified, the classifier was able to correctly identify 70% percent of walking data segments from a held-out test set as belonging to a particular subject, and 78% of those not belonging to the subject. Moreover, a number of interesting findings were identified when varying the granularity and other settings for the various discretization parameters, solidifying the work as a significant advancement in the area of behavioural analysis with sequential pattern mining and classification on time series data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering 1995, IEEE, pp 3–14

  • Ayres J, Flannick J, Gehrke J, Yiu T (2002) Sequential pattern mining using a bitmap representation. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 429–435

  • Buffett S, Emond B (2015) Using sequential pattern mining and social network analysis to identify similarities, differences and evolving behaviour in event logs. In: Business Process Management, 2015

  • Buffett S, Emond B Goutte C (2014) Using sequence classification to label behavior from sequential event logs. In: Business Process Management, 2014

  • Buffett S, Pagiatakis C, Jiang D (2018) Pattern-based behavioural analysis on neurosurgical simulation data. In: Proceedings of the Machine Learning for Healthcare Conference, 2018, pp 514–533

  • Casale P, Pujol O, Radeva P (2012) Personalization and user verification in wearable systems using biometric walking patterns. Person Ubiquitous Comput 16(5):563–580

    Article  Google Scholar 

  • Casale P, Pujol O, Radeva P (2011) Human activity recognition from accelerometer data using a wearable device. In: Iberian Conference on Pattern Recognition and Image Analysis, Springer (pp 289–296)

  • Chena YL, Kuo MH, Wub SY, Tang K (2009) Discovering recency, frequency, and monetary (rfm) sequential patterns from customers’ purchasing data. Electron Commerce Res Appl 8(5):241–251

    Article  Google Scholar 

  • Das G, Lin KI, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. KDD 98(1):16–22

    Google Scholar 

  • Dileep A, Veena T, Sekhar CC (2012) A review of kernel methods based approaches to classification and clustering of sequential patterns, part i: sequences of continuous feature vectors. In: Pattern Discovery Using Sequence Data Mining: applications and Studies 2012, IGI Global, pp 24–50

  • Egho E, Gay D, Boullé M, Voisine N, Clérot F (2015) A parameter-free approach for mining robust sequential classification rules. In: 2015 IEEE International Conference on Data Mining, IEEE, pp 745–750

  • Egho E, Gay D, Trinquart R, Boullé M, Voisine N, & Clérot F (2017) Misere-hadoop: a large-scale robust sequential classification rules mining framework. In: International conference on big data analytics and knowledge discovery, Springer, pp 105–119

  • Emond B, Buffett S (2015) Analyzing student inquiry data using process discovery and sequence classification. In: International conference on educational data mining 2015, pp 412–415

  • Emond B, Buffett S, Goutte C, Guo RJ (2015) Analysing and refining pilot training. In: International conference on educational data mining 2016, pp 682–687

  • Gracy J, Argos P (1998) Automated protein sequence database classification. i. integration of compositional similarity search, local similarity search, and multiple sequence alignment. Bioinform (Oxf, Engl) 14(2):164–173

    Article  Google Scholar 

  • Hrovat G, Fister I Jr, Yermak K, Stiglic G, Fister I (2015) Interestingness measure for mining sequential patterns in sports. J Intell Fuzzy Syst 29(5):1981–1994

    Article  Google Scholar 

  • Jaber M, Wood PT, Papapetrou P, González-Marcos A (2016) A multi-granularity pattern-based sequence classification framework for educational data. In: 2016 IEEE international conference on data science and advanced analytics (DSAA), IEEE, pp 370–378

  • Kinnebrew JS, Biswas G (2012) Identifying learning behaviors by contextualizing differential sequence mining with action features and performance evolution. In: International conference on educational data mining 2012, pp 57–64

  • Kudenko D, Hirsh H (1998) Feature generation for sequence categorization. In: Proceedings of AAAI/IAAI 1998, pp 733–738

  • Lesh N, Zaki MJ, Oglhara M (2000) Scalable feature mining for sequential data. Intell Syst Appl IEEE 15(2):48–56

    Article  Google Scholar 

  • Lesh N, Zaki MJ, Ogihara M (1999) Mining features for sequence classification. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 342–346

  • Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml

  • Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144

    Article  MathSciNet  Google Scholar 

  • Liu H, Cocea M (2019a) Granular computing-based approach of rule learning for binary classification. Granul Comput 4(2):275–283

    Article  Google Scholar 

  • Liu H, Cocea M (2019b) Nature-inspired framework of ensemble learning for collaborative classification in granular computing context. Granul Comput 4(4):715–724

    Article  Google Scholar 

  • Liu Q, Liu Q, Yang L, Wang G (2018) A multi-granularity collective behavior analysis approach for online social networks. Granul Comput 3(4):333–343

    Article  Google Scholar 

  • Li Z, Zhang A, Li D, Wang L (2007)Discovering novel multistage attack strategies. In: International conference on advanced data mining and applications, Springer, pp 45–56

  • Mooney CH, Roddick JF (2013) Sequential pattern mining-approaches and algorithms. ACM Comput Surv (CSUR) 45(2):1–39

    Article  Google Scholar 

  • Muhire B, Varsani A, Martin D (2014) SDT: a virus classification tool based on pairwise sequence alignment and identity calculation. PLoS One 9(9):1–8

    Article  Google Scholar 

  • Özdemir AT, Barshan B (2014) Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6):10691–10708

    Article  Google Scholar 

  • Padmaja TM, Bapi RS, Krishna PR (2012) Unbalanced sequential data classification using extreme outlier elimination and sampling techniques. In: Pattern discovery using sequence data mining: applications and studies 2012, IGI Global, pp 83–93

  • Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC (2001) Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: 2013 IEEE 29th international conference on data engineering (ICDE), IEEE Computer Society, pp 215–224

  • Perera D, Kay J, Koprinska I, Yacef K, Zaïane OR (2008) Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans Knowl Data Eng 21(6):759–772

    Article  Google Scholar 

  • Pradhan GN, Prabhakaran B (2017) Association rule mining in multiple, multidimensional time series medical data. J Healthcare Inf Res 1(1):92–118

    Article  Google Scholar 

  • Slim A, Heileman GL, Al-Doroubi W, Abdallah CT (2016) The impact of course enrollment sequences on student success. In: 2016 IEEE 30th International conference on advanced information networking and applications (AINA), IEEE, pp 59–65

  • Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: International conference on extending database technology. Springer, Berlin, Heidelberg, pp 1–17

  • Tseng VS, Lee CH (2009) Effective temporal data classification by integrating sequential pattern mining and probabilistic induction. Expert Syst Appl 36(5):9524–9532

    Article  Google Scholar 

  • Veena T, Dileep A, Sekhar CC (2012) A review of kernel methods based approaches to classification and clustering of sequential patterns, part ii: sequences of discrete symbols. In: Pattern discovery using sequence data mining: applications and studies, IGI Global, pp 51–71

  • Xing Z, Pei J, Keogh E (2010) A brief survey on sequence classification. ACM SIGKDD Explor Newsl 12(1):40–48

    Article  Google Scholar 

  • Xue R, Zhang T, Chen D, Le J, Lavassani M (2016) Sensor time series association rule discovery based on modified discretization method. In: 2016 First ieee international conference on computer communication and the internet (ICCCI), IEEE, pp 196–202

  • Yusof N, Zurita-Milla R, Kraak MJ, Retsios B (2016) Interactive discovery of sequential patterns in time series of wind data. Int J Geogr Inf Sci 30(8):1486–1506

    Article  Google Scholar 

  • Zaki MJ (2001) Spade: An efficient algorithm for mining frequent sequences. Mach Learn 42(1–2):31–60

    Article  Google Scholar 

  • Zhou C, Cule B, Goethals B (2015) Pattern based sequence classification. IEEE Trans Knowl Data Eng 28(5):1285–1298

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Research Council Canada, 46 Dineen Dr., Fredericton, NB, Canada

    Scott Buffett

Authors
  1. Scott Buffett
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Scott Buffett.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buffett, S. Discretized sequential pattern mining for behaviour classification. Granul. Comput. 6, 853–866 (2021). https://doi.org/10.1007/s41066-020-00234-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41066-020-00234-2

Keywords

Search

Navigation