Abstract
Data mining extracts implicit, previously unknown and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding association rules. Although a large number of researches have been devoted to this subject, to the best of our knowledge, no previous researches find association rules from genealogical data. In this paper, we use a DAG (directed acyclic graph) to represent the genealogical data of families, where a node can be viewed as a member in the family tree, the features associated with a node as the characteristics of the corresponding person and the arcs as the parental relationships between members. In the DAG, two kinds of inheritance rules are defined, which indicate how the characteristics of ancestors are passed down to descendants, and an algorithm containing four stages is proposed to discover the inheritance rules.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, M.-S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 478–499 (1994)
Liu, J., Pan, Y., Wang, K., Han, J.: Mining frequent item sets by opportunistic projection. In: Proceedings of the 2002 Int. Conf. on Knowledge Discovery in Databases (2002)
Kuok, C.M., Fu, A.W., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Record 27, 41–46 (1998)
Clementini, E., Felice, P.D., Koperski, K.: Mining multiple-level spatial association rules for objects with a broad boundary. Data and Knowledge Engineering 34, 251–270 (2000)
Wijsen, J., Meersman, R.: On the complexity of mining quantitative association rules. Data Mining and Knowledge Discovery 2, 263–281 (1998)
Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Lu, H., Feng, L., Han, J.: Beyond intra-transaction association analysis: mining multidimensional inter-transaction association rules. ACM Transactions on Information Systems 18, 423–454 (2000)
Freitas, A.A.: On rule interestingness measures. Knowledge-Based Systems 12, 309–315 (1999)
Lee, C.H., Lin, C.R., Chen, M.S.: On mining general temporal association rules in a publication database. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 337–344 (2001)
Zhuge, H.: Inheritance rules for flexible model retrieval. Decision Supports Systems 22, 379–390 (1998)
Han, J., Kamber, M.: Data Mining. Morgan Kaufmann, San Francisco (2001)
Shaffer, C.A.: A Practical Introduction to Data Structures and Algorithm Analysis. JAVA edition. Prentice Hall Inc., Englewood Cliffs (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YL., Lu, JT. (2004). Mining Inheritance Rules from Genealogical Data. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_57
Download citation
DOI: https://doi.org/10.1007/978-3-540-27772-9_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22418-1
Online ISBN: 978-3-540-27772-9
eBook Packages: Springer Book Archive