Soft Computing

, Volume 22, Issue 6, pp 1903–1919 | Cite as

A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

  • Vangipuram Radhakrishna
  • Shadi A. Aljawarneh
  • Puligadda Veereswara Kumar
  • Kim-Kwang Raymond Choo
Methodologies and Application


Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing \(2^{n }\) true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.


Temporal association pattern Monotonicity Outliers Similar Prevalence 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Borgelt C (2013) Soft pattern mining in neuroscience. In: Synergies of soft computing and statistics for intelligent data analysis, vol. 190 of the series Advances in Intelligent Systems and Computing, pp 3–10Google Scholar
  2. Chen C-H, Li A-F, Lee Y-C (2014) Actionable high-coherent-utility fuzzy itemset mining. Soft Comput 18(12):2413–2424CrossRefGoogle Scholar
  3. Chen YC, Peng WC, Lee SY (2015) Mining temporal patterns in time interval-based data. IEEE Trans Knowl Data Eng 27(12):3318–3331CrossRefGoogle Scholar
  4. Chen C-H, Lan G-C, Hong T-P, Lin S-B (2016) Mining fuzzy temporal association rules by item lifespans. Appl Soft Comput 41:265–274CrossRefGoogle Scholar
  5. Hirano S, Tsumoto S (2002) Mining similar temporal patterns in long time-series data and its application to medicine. In: Proceedings of 2002 IEEE international conference on data mining, pp 219-216Google Scholar
  6. Hong T-P, Lin K-Y, Wang S-L (2002) Mining linguistic browsing patterns in the world wide web. Soft Comput 6(5):329–336CrossRefzbMATHGoogle Scholar
  7. Hu Y-H, Tsai C-F, Tai C-T, Chiang I-C (2015) A novel approach for mining cyclically repeated patterns with multiple minimum supports. Appl Soft Comput 28:90–99 ISSN 1568-4946CrossRefGoogle Scholar
  8. Jin L, Lee Y, Seo S, Ryu KH (2006) Discovery of temporal frequent patterns using TFP-Tree. In: Management, vol 4016 of Lecture Notes in computer science, pp 349–361Google Scholar
  9. Kudłacik P, Porwik P, Wesołowski T (2016) Fuzzy approach for intrusion detection based on user’s commands. Soft Comput 20(7):2705–2719CrossRefGoogle Scholar
  10. Lin YS, Jiang JY, Lee SJ (2014) A similarity measure for text classification and clustering. IEEE Trans Knowl Data Eng 26(7):1575–1590CrossRefGoogle Scholar
  11. Mahmoud S, Lotfi A, Langensiepen C (2013) Behavioural pattern identification and prediction in intelligent environments. Appl Soft Comput 13(4):1813–1822CrossRefGoogle Scholar
  12. McClean SI, Scotney BW, Palmer FL (2013) Learning temporal concepts from heterogeneous data sequences. Soft Comput 8(2):109–117CrossRefzbMATHGoogle Scholar
  13. Peng J, Choo K-KR, Ashman H (2016) Bit-level N-Gram based forensic authorship analysis on social media: identifying individuals from linguistic profiles. J Netw Comput Appl 70:171–182CrossRefGoogle Scholar
  14. Peng J, Choo K-KR, Ashman H (2016) Astroturfing detection in social media: using binary n-gram analysis for authorship attribution. In: Proceedings of 15th IEEE international conference on trust, security and privacy in computing and communications (TrustCom 2016), pp 121–128, 23–26 August 2016. IEEE Computer Society PressGoogle Scholar
  15. Peng J, Detchon S, Choo K-KR, Ashman H, Astrofurfing detection in social media: a binary n-gram based approach. Concurr Comput Pract Exp (in press)Google Scholar
  16. Radhakrishna V, Kumar PV, Janaki V (2015) A novel approach for mining similarity profiled temporal association patterns using Venn diagrams. In: Proceedings of the international conference on engineering & MIS 2015 (ICEMIS ’15). ACM, New York, NY, USA, Article 58. doi: 10.1145/2832987.2833071
  17. Radhakrishna V, Kumar PV, Janaki V (2015) A novel approach for mining similarity profiled temporal association patterns. Rev Tec Ing Univ Zulia 38(3):80–93Google Scholar
  18. Radhakrishna V, Kumar PV, Janaki V (2015) A novel approach to discover similar temporal association patterns in a single database scan. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, 2015, pp 1–8Google Scholar
  19. Radhakrishna V, Kumar PV, Janaki V (2015) A survey on temporal databases and data mining. In: Proceedings of the international conference on engineering & MIS 2015 (ICEMIS ’15). ACM, New York, NY, USA, Article 52Google Scholar
  20. Radhakrishna V, Kumar PV, Janaki V (2015) An approach for mining similarity profiled temporal association patterns using gaussian based dissimilarity measure. In: Proceedings of the international conference on engineering & MIS 2015 (ICEMIS ’15). ACM, New York, NY, USA, Article 57Google Scholar
  21. Radhakrishna V, Kumar PV, Janaki V (2016) An approach for mining similar temporal association patterns in single database scan. In: Proceedings of first international conference on information and communication technology for intelligent systems, vol. 2, Published in Smart Innovation, Systems and Technologies 51:607–617Google Scholar
  22. Sangaiah AK, Thangavelu AK, Gao XZ, Anbazhagan N, Durai MS (2015) An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm. Appl Soft Comput 30:628–635Google Scholar
  23. Sangaiah AK, Gao XZ, Ramachandran M, Zheng X (2015) A fuzzy DEMATEL approach based on intuitionistic fuzzy information for evaluating knowledge transfer effectiveness in GSD projects. Int J Innov Comput Appl 6(3–4):203–215CrossRefGoogle Scholar
  24. Sangaiah AK, Thangavelu AK (2014) An adaptive neuro-fuzzy approach to evaluation of team- level service climate in GSD projects. Neural Comput Appl 25(3–4):573–583CrossRefGoogle Scholar
  25. Sarhadi A, Burn DH, Johnson F, Mehrotra R, Sharma A (2016) Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques. J Hydrol 536:119–132 ISSN 0022-1694CrossRefGoogle Scholar
  26. Schockaert S, De Cock M, Kerre EE (2010) Reasoning about fuzzy temporal information from the web: towards retrieval of historical events. Soft Comput 14(8):869–886CrossRefzbMATHGoogle Scholar
  27. Schultz REO, Centeno TM, Selleron G, Delgado MR (2009) A soft computing-based approach to spatio-temporal prediction. Int J Approx Reason 50(1):3–20 ISSN 0888-613XCrossRefGoogle Scholar
  28. Tseng VS, Lin KW, Chang J-C (2008) Prediction of user navigation patterns by mining the temporal web usage evolution. Soft Comput 12(2):157–163CrossRefGoogle Scholar
  29. Wan Yuqing, Gong Xueyuan, Si Yain-Whar (2016) Effect of segmentation on financial time series pattern matching. Appl Soft Comput 38:346–359CrossRefGoogle Scholar
  30. Wang H, Feng L (2016) Metric learning with geometric mean for similarities measurement. Soft Comput 20(10):3969–3979CrossRefGoogle Scholar
  31. Wang M, Ma J (2016) A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft Comput 20(10):3981–3990CrossRefGoogle Scholar
  32. Xu Z, Luo X, Liu Y, Choo K-KR, Sugumaran V, Yen N, Mei L, Hu C (2016) From latency, through outbreak, to decline: detecting different states of emergency events using web resources. IEEE Trans Big Data. doi: 10.1109/TBDATA.2016.2599935
  33. Xu Z, Xuan J, Liu Y, Choo K-KR, Mei L, Hu C (2016) Building spatial temporal relation graph of concepts pair using web repository. Inf Syst Front. doi: 10.1007/s10796-016-9676-4
  34. Yoo JS (2012) Temporal data mining: similarity-profiled association pattern. In: Data mining: foundations and intelligent paradigms, vol. 23 of intelligent systems reference library, pp 29–47Google Scholar
  35. Yoo JS, Shekhar S (2008) Mining temporal association patterns under a similarity constraint. In: Scientific and statistical database management, vol. 5069 of the series Lecture Notes in computer science, pp 401–417Google Scholar
  36. Yoo JS, Shekhar S (2009) Similarity-profiled temporal association mining. IEEE Trans Knowl Data Eng 21(8):1147–1161CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of ITVNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia
  2. 2.Department of Software EngineeringJordan University of Science and TechnologyIrbidJordan
  3. 3.Department of CSEUniversity College of Engineering, Osmania UniversityHyderabadIndia
  4. 4.Department of Information Systems and Cyber SecurityUniversity of Texas at San AntonioSan AntonioUSA
  5. 5.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia

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