R. Agrawal, T. Imielinski and A. Swami, Mining associations between sets of items in massive database, Proc. ACM SIGMOD Int. Conf. Management Data, Washington D. C., 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....occur in very few baskets. Basket data is a collection of records (or baskets) each record typically consisting of a transaction date and a collection of items (thought of as the items bought in this 12 transaction) The problem of mining association rules over basket data was introduced in [3]. Formally we consider a domain set I = fi 1 ; i m g of elements called items and we are given a set D of transactions where each transaction T is a subset of I. We say that a transaction T contains a set X of items if X T . An association rule is an implication rule X ) Y where X I ....

....constructing candidate pairs of items only if both items in the pair are frequent. Thus, to nd frequent itemsets, they proceed levelwise, nding rst the frequent items (sets of size 1) then the frequent pairs, the frequent triples, and so on. Mining for frequent itemsets An a priori algorithm [3, 5] which nds rst all frequent items needs to store them in main memory (it assumes that there is enough space) and then create candidate sets of all pairs of frequent items and nds in a second pass all frequent pairs. After the second pass it can create candidate sets of triples such that any ....

R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In SIGMOD, pages 207-216, 1993.

....H.2.8 [Database Management] Database Applications data mining ; I.2.6 [Arti cial Intelligence] Learning; H.3.3 [Information Storage and Retrieval] Information Search and Retrieval General Terms Impact Rule, Association Rule, Numeric Data, Search 1. INTRODUCTION Association rules [1] have demonstrated the ability to detect interesting associations between elds in a database. However, they utilize frequency statistics and hence have limited utility for quantitative analyses. In particular, they cannot directly segment data to optimize a numeric target. Aumann and Lindell [3] ....

....of selecting the records that satisfy the antecedent. Such a rule might be valuable for identifying classes of customers from a mailing list that might be targeted most pro tably in a mailing campaign. Aumann and Lindell [3] propose that such rules be found by identifying frequent itemsets [1] and then, treating each as an antecedent, calculating the appropriate statistics for the target. An itemset is a set of conditions. A frequent itemset is an itemset that covers at least a prede ned minimum number of training set records. The primary diculty with this approach is that frequent ....

R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In SIGMOD-93, pages 207-216, 1993.

....between heterogeneous datasets based on similarity. To the best of our knowledge, our work is the first study of such kind. 3 Association Mining and Itemset Lattice 3. 1 Association Mining The problem of finding all frequent associations among attributes in categorical ( basket ) databases [3], called association mining, is one of the most fundamental and most popular problems in data mining.We present basic concepts on association mining that are relevant to the similarity measure. The presentation here follows that of Agrawal et al. 4] Let D be a database of transactions over the ....

R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993.

....techniques have also been applied to many areas, for example, market basket data, web data, DNA data, text data, and spatial data. Association rule mining is one of the important advances in the area of data mining. The initial application of association rule mining was on market basket data [AIS93]. An association rule is a relationship of the form X= Y, where X and Y are sets of items. X is called the antecedent and Y the consequence. An example of the rule can be customers who buy diaper and milk tend to buy beer. There are two primary quality measures for each rule, support and ....

....measures for each rule, support and confidence. Support indicates the frequency of the occurring pattern while confidence indicates the strength of the implication. The goal of association rule mining is to find all the rules with support and confidence exceeding some user specified thresholds [AIS93]. The problem is typically divided into two steps. The fist first step is to find itemsets with at least minimum support. These itemsets are called frequent itemsets or large itemsets. Once the frequent itemsets are generated, the second step is to derive rules with at least minimum confidence. ....

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R. Agrawal, T. Imielinski, and A. Swami, "Mining Associations Between Sets of Items in Massive Databases," Proceedings of the ACM SIGMOD, Washington, DC, May 1993, pp. 207-216.

....in their shopping baskets. The information discovered by association rules may help to develop marketing strategies, e.g. layout optimization in supermarkets (placing milk and bread within close proximity may further encourage the sale of these items together within single visits to the store) In [1] concrete examples for extracted associations between items are given. The examples are based on supermarket products that are included in a set of transactions collected from customers purchases. One classical anectode is that diapers are purchased together with beer . For the objective of ....

Agrawal, R. and Imielinski, T. and Swami, A.: Mining Associations between Sets of Items in Massive Databases, In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993.

....algorithm. For example, the edit distance, ed, between the two lexical entries TopHotel and Top Hotel equals 1, ed( TopHotel , Top Hotel ) 1, because one insertion operation changes the string TopHotel into TopIotel . are compared, translating a numeric difference to a similarity value [0, 1] can be difficult. For example comparing the attribute population of a country a difference of 4 should yield a similarity value very close to 1, but comparing the attribute average number of children per woman the same numeric difference value should result in a similarity value close to 0. To ....

....numeric difference value should result in a similarity value close to 0. To take this into account, we first find the maximum difference between values of this attribute and then calculate the the similarity as 1 (Difference max Difference) Definition 15 (Literal similarity) slsira( A, A) [0, 1] mlsim : max sisira(A1, A2) A1 .A A A2 .A lsira(Ai, Aj, A) slsim(Ai, Aj) mlsim(A) And last but not least, unlike for relations the minimal similarity when comparing attributes is always zero. Definition 16 (Similarity for one attribute) if As(A , 11) 0 V As(A, 12) 0 (a As( ....

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R. Agrawal, T. Imielinski, and A. Swami. Mining Associations between Sets of Items in Massive Databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993, pages 688-692. ACM Press, 1993.

....to performing the type of contrast analysis for which contrast sets were designed. Magnum Opus is a general purpose rule discovery system. It implements the OPUS AR rule discovery algorithm [14] It provides association rule like functionality, but does not use the frequent itemset strategy [1] and hence does not require the specification of a minimum support constraint. C4.5rules derives classification rules by first learning a decision tree and then transforming that tree into a rule format. It and the other two systems are described in more detail in Section 2. To evaluate the ....

R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the 1993.

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R. Agrawal, T. Imielinski and A. Swami, Mining associations between sets of items in massive database, Proc. ACM SIGMOD Int. Conf. Management Data, Washington D. C., 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993.

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Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining associations between sets of items in massive databases. Proc. of ACM SIGMOD.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proc. of ACM SIGMOD, 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993.

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Agrawal, R., Imielinski, T. & Swami, A. (1993), Mining Associations between Sets of Items in Massive Databases, In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C., pages 207--216.

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R. Agrawal, T. Imielinski, and A. Swami. Mining Association between Sets of Items in Massive Databases. In ACM-SIGMOD 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining Association between Sets of Items in Massive Databases. In ACMSIGMOD 1993.

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R. Agrawal, T. Imielinski and A. Swami. Mining Associations between Sets of Items in Massive Databases. In Proc. of ACM SIGMOD, pages 207--216, Washington D.C, May 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proc. 1993.

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Agrawal, R., Imielinski, T., and Swami, A. Mining associations between sets of items in massive databases. In Proceedings of the 1993.

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T. Imielinski R. Agrawal and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C., pages 207--216, May 1993. 34

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R. Agrawal, T. Imielinski, and A. Swami. Mining Association between Sets of Items in Massive Databases. In ACM-SIGMOD 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. SIGMOD Record, 22(2):207-216, June 1993.

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Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining associations between sets of items in massive databases. Proc. of ACM SIGMOD.

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R. Agrawal, T. Imielinski and A. Swami, Mining associations between sets of items in massive database, Proc. ACM SIGMOD Int. Conf. Management Data, Washington D. C., 1993.

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R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, 1993.

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