Advertisement

Applied Intelligence

, Volume 48, Issue 11, pp 4515–4529 | Cite as

Observation of sales trends by mining emerging patterns in dynamic markets

  • Cheng-Hsiung Weng
  • Tony, Cheng-Kui HuangEmail author
Article
  • 124 Downloads

Abstract

One of the regular tasks for managers is to formulate various marketing strategies to confront competitive and changing market environments. Therefore, identifying product sales trends in order to evolve a better strategy is an important issue. It is also an issue that disturbs managers because the preferences of customers often change and capturing them is not an effortless process. In the data mining field, emerging patterns (EPs) are defined as itemsets of which supports increase significantly from one dataset to another. EPs, therefore, can capture emerging trends in timestamped databases or useful contrasts between data classes. In this study, we integrate the concepts of product life cycle and emerging patterns to define four emerging patterns, referred to here as Growth patterns, Rapid-Rise patterns, Decline patterns, and Rapid-Sink patterns, so as to identify interesting sales trends. In addition, we propose a new approach to discover these four emerging patterns from two real-life datasets. Experimental results show that the proposed approach can help managers identify these four types of patterns and interesting trends.

Keywords

Data mining Emerging patterns Product life cycle Sales trends 

Notes

Acknowledgments

The authors would like to thank the Editor-in-Chief, Dr. Moonis Ali, and the anonymous referees for their helps and valuable comments to improve this paper. This research was supported by the Ministry of Science and Technology of the Republic of China under the grants MOST 105-2410-H-166-002 and MOST 106-2410-H-194-026-MY2.

References

  1. 1.
    Aggarwal CC, Yu PS (1998) A new framework for itemset generation. In: Proceedings of the ACM symposium on principles of database systems. Seattle, pp 18–24Google Scholar
  2. 2.
    Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD. Washington, DC, pp 207–216Google Scholar
  3. 3.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the VLDB conference, pp 487–499Google Scholar
  4. 4.
    Alavi F, Hashemi S (2015) DFP-SEPSF: a dynamic frequent pattern tree to mine strong emerging patterns in streamwise features. Eng Appl Artif Intell 37:54–70CrossRefGoogle Scholar
  5. 5.
    Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD international conference on management of data. Tucson, pp 265–276Google Scholar
  6. 6.
    Ceci M, Appice A, Malerba D (2007) Discovering emerging patterns in spatial databases: a multi-relational approach. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin, pp 390–397Google Scholar
  7. 7.
    Ceci M, Appice A, Caruso C, Malerba D (2008) Discovering emerging patterns for anomaly detection in network connection data. In: International symposium on methodologies for intelligent systems. Springer, Berlin, pp 179–188Google Scholar
  8. 8.
    Chen YL, Weng CH (2008) Mining association rules from imprecise ordinal data. Fuzzy Sets Syst 159 (4):460–474MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chen CL, Tseng FSC, Liang T (2010) Mining fuzzy frequent itemsets for hierarchical document clustering. Inf Process Manag 46(2):193–211CrossRefGoogle Scholar
  10. 10.
    Chu CJ, Tseng VS, Liang T (2009) RETRACTED: efficient mining of temporal emerging itemsets from data streams. Expert Syst Appl 36(1):885–893CrossRefGoogle Scholar
  11. 11.
    Ciampi A, Fumarola F, Appice A, Malerba D (2009) Approximate frequent itemset discovery from data stream. In: Congress of the Italian association for artificial intelligence. Springer, Berlin, pp 151–160CrossRefGoogle Scholar
  12. 12.
    Deng K, Zaïane OR (2010) An occurrence based approach to mine emerging sequences. Springer, Berlin, pp 275–284Google Scholar
  13. 13.
    Ding G, Wang J, Qin K (2010) A visual word weighting scheme based on emerging itemsets for video annotation. Inf Process Lett 110(16):692–696CrossRefGoogle Scholar
  14. 14.
    Dong G, Bailey J (2012) Contrast data mining: concepts, algorithms, and applications. CRC Press, Boca RatonGoogle Scholar
  15. 15.
    Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 43–52Google Scholar
  16. 16.
    García-Vico AM, Carmona CJ, Martín D, García-Borroto M, del Jesus MJ (2018) An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects, vol 8, no 1. Wiley Interdisciplinary Reviews: Data Mining and Knowledge DiscoveryGoogle Scholar
  17. 17.
    Han JW, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  18. 18.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: ACM sigmod record, vol 29, no 2. ACM, pp 1–12Google Scholar
  19. 19.
    Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Discov 15(1):55–86MathSciNetCrossRefGoogle Scholar
  20. 20.
    Huang Z, Gan C, Lu X, Huan H (2013) Mining the changes of medical behaviors for clinical pathways. Stud Health Technol Inf 192(1–2):117–121Google Scholar
  21. 21.
    Khan MS, Coenen F, Reid D, Patel R, Archer L (2010) A sliding windows based dual support framework for discovering emerging trends from temporal data. Knowl-Based Syst 23(4):316–322CrossRefGoogle Scholar
  22. 22.
    Kim JK, Song HS, Kim TS, Kim HK (2005) Detecting the change of customer behavior based on decision tree analysis. Expert Syst 22(4):193–205CrossRefGoogle Scholar
  23. 23.
    Kotler P, Keller KL (2003) A framework for marketing management, 4th edn. Pearson International EducationGoogle Scholar
  24. 24.
    Lee VE, Jin R, Agrawal G (2014) Frequent pattern mining in data streams. Springer, Cham, pp 199–224Google Scholar
  25. 25.
    Li J, Wong L (2002) Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics 18(5):725–734CrossRefGoogle Scholar
  26. 26.
    Li J, Dong G, Ramamohanarao K (2000) Instance-based classification by emerging patterns. In: Principles of data mining and knowledge discovery. Springer, Berlin, pp 191–200Google Scholar
  27. 27.
    Li J, Dong G, Ramamohanarao K (2001) Making use of the most expressive jumping emerging patterns for classification. Knowl Inf Syst 3(2):131–145CrossRefGoogle Scholar
  28. 28.
    Li G, Law R, Vu H Q, Rong J, Zhao XR (2015) Identifying emerging hotel preferences using emerging pattern mining technique. Tourism Manag 46:311–321CrossRefGoogle Scholar
  29. 29.
    Nasreen S, Azam MA, Shehzad K, Naeem U, Ghazanfar MA (2014) Frequent pattern mining algorithms for finding associated frequent patterns for data streams: a survey. Procedia Comput Sci 37:109–116CrossRefGoogle Scholar
  30. 30.
    Pei J, Han J, Lu H, Nishio S, Tang S, Yang D (2001) H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings IEEE international conference on data mining, 2001. ICDM 2001. IEEE, pp 441–448Google Scholar
  31. 31.
    Shie BE, Philip SY, Tseng VS (2013) Mining interesting user behavior patterns in mobile commerce environments. Appl Intell 38(3):418–435CrossRefGoogle Scholar
  32. 32.
    Terlecki P, Walczak K (2007) Jumping emerging patterns with negation in transaction databases–classification and discovery. Inf Sci 177(24):5675–5690MathSciNetCrossRefGoogle Scholar
  33. 33.
    Tsai CY, Shieh YC (2009) A change detection method for sequential patterns. Decis Support Syst 46 (2):501–511CrossRefGoogle Scholar
  34. 34.
    Vimieiro R, Moscato P (2014) A new method for mining disjunctive emerging patterns in high-dimensional datasets using hypergraphs. Inf Syst 40:1–10CrossRefGoogle Scholar
  35. 35.
    Wang L, Zhao H, Dong G, Li J (2005) On the complexity of finding emerging patterns. Theor Comput Sci 335(1):15–27MathSciNetCrossRefGoogle Scholar
  36. 36.
    Weng CH (2011) Mining Fuzzy specific rare itemsets for education data. Knowl-Based Syst 24(5):697–708CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Management Information SystemsCentral Taiwan University of Science and TechnologyTaichungRepublic of China
  2. 2.Department of Computer Science and Information EngineeringNational Chin-Yi University of TechnologyTaichungRepublic of China
  3. 3.Department of Business AdministrationNational Chung Cheng UniversityChia-YiRepublic of China

Personalised recommendations