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Download Deductive and Object-Oriented Databases: Third International by David Maier, Judith B. Cushing (auth.), Stefano Ceri, PDF

By David Maier, Judith B. Cushing (auth.), Stefano Ceri, Katsumi Tanaka, Shalom Tsur (eds.)

This quantity comprises the court cases of the 3rd foreign convention on Deductive and Object-Oriented Databases. Its crucial guiding principle is that the object-oriented and deductive paradigms for modeling, organizing, and processing information supplement one another, instead of competing, and that difficulties related to enormous volumes of advanced facts can most sensible be solved by way of integrating the simplest of either techniques. significant questions within the region are: - How can we layout a device that offers the simplest of the object-oriented and declarative principles? - How can the clients of this instrument convey their difficulties in a mix of declarative and procedural positive aspects? the quantity comprises 29 papers that give a contribution in the direction of answering those questions.

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Read or Download Deductive and Object-Oriented Databases: Third International Conference, DOOD'93 Phoenix, Arizona, USA, December 6–8, 1993 Proceedings PDF

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Extra info for Deductive and Object-Oriented Databases: Third International Conference, DOOD'93 Phoenix, Arizona, USA, December 6–8, 1993 Proceedings

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Estimating joint probabilities without combinatory counting. Technical Report TR-764, Computer Science Department,University of Rochester, 2002. 10. C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal structures. In Proc. of VLDB, 1998. tw Abstract. In this paper, we devise an efficient algorithm for clustering market-basket data items. Market-basket data analysis has been well addressed in mining association rules for discovering the set of large items which are the frequently purchased items among all transactions.

Ng. The segment support map: Scalable mining of frequent itemsets. SIGKDD Explorations, 2(2), December 2000. 9. Tao Li, Shenghuo Zhu, Mitsunori Ogihara, and Yinhe Cheng. Estimating joint probabilities without combinatory counting. Technical Report TR-764, Computer Science Department,University of Rochester, 2002. 10. C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal structures. In Proc. of VLDB, 1998. tw Abstract. In this paper, we devise an efficient algorithm for clustering market-basket data items.

Market-basket data analysis has been well addressed in mining association rules for discovering the set of large items which are the frequently purchased items among all transactions. In essence, clustering is meant to divide a set of data items into some proper groups in such a way that items in the same group are as similar to one another as possible. In view of the nature of clustering market basket data, we present a measurement, called the small-large (SL) ratio, which is in essence the ratio of the number of small items to that of large items.

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