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2015 | OriginalPaper | Buchkapitel

4. Measuring Association Among Items in a Database

verfasst von : Animesh Adhikari, Jhimli Adhikari

Erschienen in: Advances in Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Measuring association among variables is an important step for finding solutions to many data mining problems. An existing metric might not be effective to serve as a measure of association among a set of items in a database. In this chapter, we propose two measures of association, A1 and A2. We introduce the notion of associative itemset in a database. We express the proposed measures in terms of supports of itemsets. In addition, we provide theoretical foundations of our work. We present experimental results on both real and synthetic databases to show the effectiveness of A 2.

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Fußnoten
Literatur
Zurück zum Zitat Adhikari A, Rao PR (2007) Study of select items in multiple databases by grouping. In: Proceedings of 3rd Indian international conference on artificial intelligence, pp 1699–1718 Adhikari A, Rao PR (2007) Study of select items in multiple databases by grouping. In: Proceedings of 3rd Indian international conference on artificial intelligence, pp 1699–1718
Zurück zum Zitat Adhikari A, Rao PR (2008) Efficient clustering of databases induced by local patterns. Decis Support Syst 44(4):925–943CrossRef Adhikari A, Rao PR (2008) Efficient clustering of databases induced by local patterns. Decis Support Syst 44(4):925–943CrossRef
Zurück zum Zitat Aggarwal C, Yu P (1998) A new framework for itemset generation. In: Proceedings of PODS, pp 18–24 Aggarwal C, Yu P (1998) A new framework for itemset generation. In: Proceedings of PODS, pp 18–24
Zurück zum Zitat Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD conference management of data, pp 207–216 Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD conference management of data, pp 207–216
Zurück zum Zitat Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the international conference on very large databases, pp 487–499 Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the international conference on very large databases, pp 487–499
Zurück zum Zitat Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Proceedings of SIGMOD conference, pp 255–264 Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Proceedings of SIGMOD conference, pp 255–264
Zurück zum Zitat Han J, Pei J, Yiwen Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD conference management of data, pp 1–12 Han J, Pei J, Yiwen Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD conference management of data, pp 1–12
Zurück zum Zitat Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kauffmann Publishers, Burlington Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kauffmann Publishers, Burlington
Zurück zum Zitat Hershberger SL, Fisher DG (2005) Measures of association, Encyclopedia of statistics in behavioral science. Wiley, London Hershberger SL, Fisher DG (2005) Measures of association, Encyclopedia of statistics in behavioral science. Wiley, London
Zurück zum Zitat Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef
Zurück zum Zitat Omiecinski ER (2003) Alternative interest measures for mining associations in databases. IEEE Trans Knowl Data Eng 15(1):57–69CrossRefMathSciNet Omiecinski ER (2003) Alternative interest measures for mining associations in databases. IEEE Trans Knowl Data Eng 15(1):57–69CrossRefMathSciNet
Zurück zum Zitat Palshikar GK, Kale MS, Apte MM (2005) Association rule mining using heavy itemsets. In: Proceedings of international conference on management of data, pp 148–155 Palshikar GK, Kale MS, Apte MM (2005) Association rule mining using heavy itemsets. In: Proceedings of international conference on management of data, pp 148–155
Zurück zum Zitat Papoulis A (1984) Probability, random variables and stochastic processes, 2 edn. McGraw-Hill, New York Papoulis A (1984) Probability, random variables and stochastic processes, 2 edn. McGraw-Hill, New York
Zurück zum Zitat Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. In: Proceedings of knowledge discovery in databases, pp 229–248 Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. In: Proceedings of knowledge discovery in databases, pp 229–248
Zurück zum Zitat Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st international conference on very large data bases, pp 432–443 Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st international conference on very large data bases, pp 432–443
Zurück zum Zitat Tan P-N, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of SIGKDD conference, pp 32–41 Tan P-N, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of SIGKDD conference, pp 32–41
Zurück zum Zitat Wu X, Zhang C, Zhang S (2005) Database classification for multi-database mining. Inf Syst 30(1):71–88CrossRefMATH Wu X, Zhang C, Zhang S (2005) Database classification for multi-database mining. Inf Syst 30(1):71–88CrossRefMATH
Zurück zum Zitat Xin D, Han J, Yan X, Cheng H (2005) Mining compressed frequent-pattern sets. In: Proceedings of the 31st VLDB conference, pp 709–720 Xin D, Han J, Yan X, Cheng H (2005) Mining compressed frequent-pattern sets. In: Proceedings of the 31st VLDB conference, pp 709–720
Metadaten
Titel
Measuring Association Among Items in a Database
verfasst von
Animesh Adhikari
Jhimli Adhikari
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-13212-9_4