Relational peculiarity-oriented mining | Data Mining and Knowledge Discovery Skip to main content
Springer Nature Link
Log in

Relational peculiarity-oriented mining

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

Peculiarity rules are a new type of useful knowledge that can be discovered by searching the relevance among peculiar data. A main task in mining such knowledge is peculiarity identification. Previous methods for finding peculiar data focus on attribute values. By extending to record-level peculiarity, this paper investigates relational peculiarity-oriented mining. Peculiarity rules are mined, and more importantly explained, in a relational mining framework. Several experiments are carried out and the results show that relational peculiarity-oriented mining is effective.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Aronis JM, Kolluri V, Foster JP, Buchanan BG (1997) The WoRLd: knowledge discovery from multiple distributed databases, In: Proceedings of the 10th international Florida AI research symposium, pp 337–341

  • Chen H, Wang FY (2005) Artificial intelligence for homeland security. IEEE Intell Syst 20:12–16

    Article  Google Scholar 

  • Han JW, Kamber M (2001) Data mining concepts and techniques. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Kerber R (1992) ChiMerge: discretization of numeric attributes. In: Proceedings of the 9th international conference on artificial intelligence. AAAI, pp 123–128

  • Lin TY (1998) Granular computing on binary relations 1: data mining and neighborhood systems. In: Polkowski L, Skowron A (eds) Rough sets in knowledge discovery 1. Springer, Berlin, pp. 107–121

    Google Scholar 

  • Liu C, Zhong N (2001) Rough problem settings for ILP dealing with imperfect data. Comput Intel 17:446–459

    Article  MathSciNet  Google Scholar 

  • Liu H, Lu H, Yao J (1998) Identifying relevant databases for multidatabases mining. In: Proceedings of the 2nd Pacific–Asia conference knowledge discovery and data mining, LNAI 1394, Springer, Berlin, pp 210–221

  • Liu H, Setiono R (1997) Feature selection via discretization of numeric attributes. IEEE Trans Knowl Data Eng 9:642–645

    Article  Google Scholar 

  • Muggleton S (1995) Inverse entailment and progol. New Generat Comput 13:245–286

    Google Scholar 

  • Nienhuys-Chen SH, de Wolf R (1997) Foundations of inductive logic programming, LNAI 1228, Springer, Berlin, pp 163–177

  • Ohshima M, Zhong N, Yao YY, Murata S (2004) Peculiarity oriented analysis in multi-people tracking images. In: Dai H et al (eds) Advances in knowledge discovery and data mining (PAKDD’04), LNAI 3056, Springer, Berlin, pp 508–518

  • Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266

    Google Scholar 

  • Quinlan JR, Cameron-Jones RM (1995) Induction of logic programs: FOIL and related systems. New Generat Comput 13:287–312

    Article  Google Scholar 

  • Ribeiro JS, Kaufman KA, Kerschberg L (1995) Knowledge discovery from multiple databases. In: Proceedings of the 1st international conference on knowledge discovery and data mining. AAAI, pp 240–245

  • Saso D (2001) Data mining in a nutshell. In: Saso D, Nada L (eds) Relational data mining. Springer, Berlin, pp 3–26

    Google Scholar 

  • Saso D, Nada L (2001) An introduction to inductive logic programming. In: Saso D, Nada L (eds) Relational data mining. Springer, Berlin, pp 48–73

    Google Scholar 

  • Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Slowinski R (eds) Intelligent decision support. Kluwer, Amsterdam, pp 331–362

    Google Scholar 

  • Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Komorowski J, Zytkow J (eds) Principle of data mining and knowledge discovery, LNAI 1263, Springer, Berlin, pp 367–375

  • Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 15:353–367

    Article  Google Scholar 

  • Yao YY (1999) Granular computing using neighborhood systems. In: Roy R, Furuhashi T, Chawdhry PK (eds) Advances in soft computing: engineering design and manufacturing. Springer, Berlin, pp 539–553

    Google Scholar 

  • Yao YY, Zhong N (2002) An analysis of peculiarity factor in peculiarity oriented data mining. In: Lin TY, Ohsuga S (eds) Proceedings of the 2002 ICDM workshop on foundation of data mining and knowledge discovery (FDM’02). pp 185–188

  • Yao YY, Zhao Y, Maguire RB (2003) Explanation oriented association mining using rough set theory. In: Proceedings of the 9th international workshop on rough sets, fuzzy sets, data mining, and granular computing, LNAI 2639, Springer, Berlin, pp 165–172

  • Yao YY, Wang FY, Wang J, Zeng D (2005) Rule + exception strategies for security information analysis. IEEE Intell Syst 20:52–57

    Article  Google Scholar 

  • Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127

    Article  MATH  MathSciNet  Google Scholar 

  • Zheng L, Liu C, Dong J, Zhong N (2005) Generating numerical constraints in CILP. Int J Pattern Recogn 19:91–108

    Article  Google Scholar 

  • Zhong N, Dong JZ, Ohsuga S (2001) Using rough sets with heuristics to feature selection. J Intell Inf Syst 16:199–214

    Article  MATH  Google Scholar 

  • Zhong N, Ohshima M, Ohsuga S (2001) Peculiarity oriented mining and its application for knowledge discovery in amino-acid data. In: Cheung D et al (eds) Advances in knowledge discovery and data mining, LNAI 2035, Springer, Berlin, pp 260–269

  • Zhong N, Yao YY, Ohshima M, Ohsuga S (2001) Interestingness, peculiarity, and multi-database mining. In: Proceedings of the 2001 IEEE international conference on data mining. IEEE Computer Society, pp 566–573

  • Zhong N, Yao YY, Ohshima M (2003) Peculiarity oriented multidatabase mining. IEEE Trans Knowl Data Eng 4:952–960

    Article  Google Scholar 

  • Zhong N, Liu C, Yao YY, Ohshima M, Huang MX, Huang JJ (2004) Relational peculiarity oriented data mining. In: Proceedings of the 2004 IEEE international conference on data mining. IEEE Computer Society, pp 575–578

  • Zhong N, Motomura S, Wu JL (2005) Peculiarity oriented multi-aspect brain data analysis for studying human multi-perception mechanism. In: Proceedings of the SAINT 2005 workshops (Workshop 8: computer intelligence for exabyte scale data explosion). IEEE Computer Society, pp 306–309

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi, 371-0816, Japan

    Muneaki Ohshima & Ning Zhong

  2. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    YiYu Yao

  3. Computer Science College, Beijing University of Technology, Beijing, 100022, China

    Chunnian Liu

Authors
  1. Muneaki Ohshima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YiYu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunnian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Zhong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ohshima, M., Zhong, N., Yao, Y. et al. Relational peculiarity-oriented mining. Data Min Knowl Disc 15, 249–273 (2007). https://doi.org/10.1007/s10618-006-0046-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-006-0046-6

Keywords

Search

Navigation